From a403fa0226d97ea8daf64e450db63c25234a7e5b Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 19 Jul 2026 04:21:09 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 84 + generation_config.json | 12 + model.safetensors | 3 + ...59728.ubuntu-System-Product-Name.1383912.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 4631 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 33 + trainer_state.json | 267 + training_args.bin | 3 + training_loss.png | Bin 0 -> 51273 bytes 17 files changed, 5317 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 experiment_recipe.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul15_04-08-48_ubuntu-System-Product-Name/events.out.tfevents.1784059728.ubuntu-System-Product-Name.1383912.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..5f3adcb --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v29_v19_user_world_guard_lr8e7_ep018 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2 + results: [] +--- + + + +# v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2 + +This model is a fine-tuned version of [lldois/v29_v19_user_world_guard_lr8e7_ep018](https://huggingface.co/lldois/v29_v19_user_world_guard_lr8e7_ep018) on the exp_v39_curriculum_r3 dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607191 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 0.8 + +### Training results + + + +### Framework versions + +- Transformers 5.6.0 +- Pytorch 2.7.1+cu126 +- Datasets 4.0.0 +- Tokenizers 0.22.2 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..269952a --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.802937576499388, + "total_flos": 1.5578264772983194e+17, + "train_loss": 2.1087728360804117, + "train_runtime": 2487.2809, + "train_samples_per_second": 0.526, + "train_steps_per_second": 0.132 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + 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"fb38ffd99b1ed61bbca834a85af32e3bb557aeba6a8b05f134ab5b99c16a22da", + "requested_components": { + "general": 22000, + "r0": 6000, + "r3_think": 16000, + "r3_direct": 18000, + "old_rec": 3000, + "old_user": 2500, + "old_item": 2500 + }, + "groups": { + "general": 22000, + "r3": 34000, + "old_rec": 3000, + "old_user": 2500, + "r0": 6000, + "old_item": 2500 + }, + "variants": { + "official_general_direct": 10947, + "profile_live_direct": 4236, + "profile_video/ad_cot": 3722, + "profile_live_cot": 3715, + "original_competition": 3943, + "user_strict_logic": 357, + "profile_video/video_direct": 5603, + "caption_to_sid_direct": 2978, + "profile_video/video_cot": 4939, + "profile_goods_cot": 3624, + "official_general_cot": 11053, + "profile_video/ad_direct": 4171, + "profile_goods_direct": 3990, + "rec_no_think_direct_final": 1484, + "sid_to_caption_cot": 1530, + "sid_to_caption_direct": 1492, + "item_no_think_direct_final": 429, + "user_array_json_strict": 402, + "item_short_think": 417, + "user_logic_json_strict": 364, + "user_strict_array": 429, + "user_extra_no_think_logic": 175 + }, + "routes": { + "no_think": 38178, + "think": 31822 + }, + "target_leakage": 0, + "seed": 202607157 + }, + "learning_rate": "5.0e-7", + "epochs": 0.8, + "stages": [ + { + "dataset": "v39_curriculum_r0", + "lr": "6.0e-7", + "epochs": 0.8 + }, + { + "dataset": "v39_curriculum_r3", + "lr": "5.0e-7", + "epochs": 0.8 + } + ], + "note": "Two-stage curriculum: 0.8 epoch R0/general alignment followed by 0.8 epoch R3/general cognition.", + "cot_policy": "Preserve meaningful CoT for /think and pure final outputs for /no_think.", + "checkpoint_policy": "save_strategy=no; only the final model is retained.", + "script": "/home/ll/llm4rec/experiments/run_v34_v43.py", + "script_sha256": "fbf01773c61579cf6159f64442fc0c8902714c84ed23b488bc9a6236a154f402", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1.yaml", + "/home/ll/llm4rec/experiments/configs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage2.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_v34_v43.py --single v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2 --gpu " +} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..6f06785 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0687b6847e3ba3f043b60de783a366d09423f9bf19cdc15815f6dd818e0268cf +size 1602902832 diff --git a/runs/Jul15_04-08-48_ubuntu-System-Product-Name/events.out.tfevents.1784059728.ubuntu-System-Product-Name.1383912.0 b/runs/Jul15_04-08-48_ubuntu-System-Product-Name/events.out.tfevents.1784059728.ubuntu-System-Product-Name.1383912.0 new file mode 100644 index 0000000..d56f9f1 --- /dev/null +++ b/runs/Jul15_04-08-48_ubuntu-System-Product-Name/events.out.tfevents.1784059728.ubuntu-System-Product-Name.1383912.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6026fe48374d3b721987f3c83ac4c6688338814b5937192b4d0805033d527b20 +size 13022 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..35d445f --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": true, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..57aeabe --- /dev/null +++ b/train.log @@ -0,0 +1,4631 @@ +[2026-07-15 03:27:21] stage 1/2: /home/ll/llm4rec/experiments/configs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1.yaml +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-15 03:27:24] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:769] 2026-07-15 03:27:24,631 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 03:27:24,634 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-15 03:27:25,292 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 03:27:25,293 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-15 03:27:25,293 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 03:27:25,294 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + 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00:14 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:24 [视频-点赞/长播] <|video_begin|> + 00:24 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 06:41 [视频-点赞/长播] <|video_begin|> + 06:42 [视频-点赞/长播] <|video_begin|> + 06:43 [视频-点赞/长播] <|video_begin|> + 07:25 [视频-点赞/长播] <|video_begin|> + 07:25 [视频-长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-点赞/长播] <|video_begin|> + 16:08 [视频-点赞/长播] <|video_begin|> 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[视频-评论/点赞/长播] <|video_begin|> + 00:01 [视频-评论/点赞/长播] <|video_begin|> + 00:05 [视频-点赞/长播] <|video_begin|> + 00:05 [视频-长播] <|video_begin|> + 00:09 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:44 [广告-点击] <|ad_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 11:20 [视频-长播] <|video_begin|> + 12:33 [视频-点赞/长播] <|video_begin|> + 12:33 [视频-点赞/长播] <|video_begin|> + 12:33 [视频-评论/点赞/长播] <|video_begin|> + 13:19 [视频-点赞/长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 与 交互 A 必须在同一生活场景、主题类目下具有极强的需求补完关系。 +2.兴趣因果递进：交互 B 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下，交互 B 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请围绕以下主题提取行为逻辑链： +主题：从被动接收信息到主动实践的手工兴趣唤醒 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收信息到主动实践的手工兴趣唤醒", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这位用户的行为。这项任务要求我基于用户的交互历史，挖掘出一条从被动接收信息到主动实践的手工兴趣唤醒的行为链。我需要重点关注行为之间的时间顺序和逻辑关联，确保后续行为是由前序行为引发的。 + +我首先对用户的整体行为进行了梳理。从2025年7月到2026年初，我注意到这位用户的消费行为非常丰富，涵盖了食品零食、服饰鞋包、美妆护肤、家居日用以及一些健身器材等多个品类。这表明她是一位乐于尝试和消费的年轻女性。在内容消费方面，尤其是在2026年1月底到2月初，她密集地观看了大量视频，内容五花八门，包括搞笑短剧、明星八卦、社会新闻、情感故事、影视剪辑和生活技巧等，显示出她兴趣广泛，但似乎没有特别聚焦的领域。 + +我的分析重点放在了寻找一个从“看”到“做”的转变点上。在浏览2026年2月3日的行为时，我发现了一个非常清晰的信号。在当天上午08:20，用户观看并评论了一个展示手工编织围巾的视频 <|video_begin|>。这个视频强调了手工制品所蕴含的“情感价值”和“人情味”，这是一种情感层面的触动，而不仅仅是展示商品。我推测，这种情感化的内容很可能激发了用户自己动手制作的念头。 + +紧接着，我发现用户的行为立刻印证了我的推测。就在观看完视频之后，同一天内，她开始密集地点击与“手工编织”相关的商品。例如，她点击了标题中包含“手工长绒毛围巾diy自制编织心意礼物”的商品 <|prod_begin|>，以及多款以“蜡笔小新”为主题的DIY围巾材料包。这些点击行为中的关键词，如“diy”、“手工编织”、“心意礼物”，与她刚刚观看的视频内容高度契合。这表明她的兴趣已经从被动的观看，迅速转化为主动的、有明确目的的商品调研。她不再只是欣赏成品，而是在寻找能够让她亲身实践的工具和材料。 + +最终，在经过一番浏览和比较之后，用户在当天就完成了购买决策。她购买了一款名为“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包”的商品 <|prod_begin|>。这个购买行为是整个兴趣链条的关键闭环，标志着她完成了从被动的信息接收者到主动的实践者的身份转变。整个过程从看到视频激发兴趣，到搜索和比较DIY材料，再到最终下单购买，逻辑清晰，时间紧凑，构成了一条完整的兴趣唤醒与实践路径。 + +综合以上分析，我认为用户的行为清晰地展示了从被动接收信息到主动实践的手工兴趣唤醒过程。这个过程始于观看一个强调情感价值的手工编织视频 <|video_begin|>，这激发了她的制作兴趣；随后，她立即开始浏览具体的DIY围巾材料包商品，例如点击了“手工长绒毛围巾diy自制编织心意礼物” <|prod_begin|>，将模糊的兴趣转化为明确的行动意图；最终，通过购买“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包” <|prod_begin|>，完成了从认知到行为的转化。因此，未来向她推荐更多DIY手工制作类的内容或商品，有较大概率被吸引。 +{"logic_chain": {"name": "从被动接收信息到主动实践的手工兴趣唤醒", "events": [{"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣触发：被动观看到手工编织围巾的视频，内容强调“情感价值”和“人情味”，可能激发了用户的制作兴趣。"}, {"date": "2026-02-03", "action": "[商品-点击] <|prod_begin|>", "logic": "意图确认与调研：观看视频后，用户立即开始浏览具体的“DIY材料包”商品，关键词“手工”、“diy自制”、“心意礼物”与视频内容高度关联，表明兴趣已转化为具体行动意图。"}, {"date": "2026-02-03", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在浏览了多种DIY围巾材料包后，用户最终完成购买，实现了从“被动观看”到“主动实践”的认知与行为跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-24】 + --:-- [直播-关注] <|living_begin|> +【2025-02-08】 + --:-- [直播-关注] <|living_begin|> +【2025-02-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-03-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-10】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> +【2025-07-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + 00:02 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 13:36 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 02:06 [视频-关注/长播] <|video_begin|> + 02:06 [视频-关注/点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-23】 + 10:34 [视频-点赞/长播] <|video_begin|> + 11:08 [视频-收藏/长播] <|video_begin|> + 19:11 [广告-深度转化] <|ad_begin|> + 19:45 [广告-深度转化] <|ad_begin|> + 22:23 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 00:18 [广告-深度转化] <|ad_begin|> + 00:59 [视频-关注/长播] <|video_begin|> + 16:09 [视频-关注/点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-25】 + 14:28 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + 07:36 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 10:19 [广告-深度转化] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> + 20:40 [广告-深度转化] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 02:30 [广告-深度转化] <|ad_begin|> + 03:08 [广告-深度转化] <|ad_begin|> + 03:55 [广告-深度转化] <|ad_begin|> + 04:37 [广告-深度转化] <|ad_begin|> + 08:09 [视频-关注/收藏/点赞/长播] <|video_begin|> + 11:49 [广告-深度转化] <|ad_begin|> + 16:46 [广告-深度转化] <|ad_begin|> + 20:44 [广告-深度转化] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> +【2026-01-28】 + 00:56 [广告-深度转化] <|ad_begin|> + 01:37 [广告-点击] <|ad_begin|> + 01:41 [广告-点击] <|ad_begin|> + 01:44 [广告-深度转化] <|ad_begin|> + 01:44 [广告-点击] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 02:04 [广告-点击] <|ad_begin|> + 04:18 [视频-收藏/长播] <|video_begin|> + 04:47 [广告-深度转化] <|ad_begin|> + 04:47 [广告-点击] <|ad_begin|> + 05:49 [广告-深度转化] <|ad_begin|> + 05:49 [广告-点击] <|ad_begin|> + 08:53 [广告-深度转化] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 09:37 [广告-点击] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> + 12:16 [广告-深度转化] <|ad_begin|> + 12:16 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + 00:30 [广告-深度转化] <|ad_begin|> + 00:30 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:05 [广告-深度转化] <|ad_begin|> + 06:05 [广告-点击] <|ad_begin|> + 10:17 [广告-深度转化] <|ad_begin|> + 10:17 [广告-点击] <|ad_begin|> + 12:47 [视频-关注/长播] <|video_begin|> + 12:47 [视频-关注/长播] <|video_begin|> + 13:59 [广告-深度转化] <|ad_begin|> + 13:59 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:31 [广告-深度转化] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:11 [广告-深度转化] <|ad_begin|> + 00:11 [广告-点击] <|ad_begin|> + 05:12 [广告-点击] <|ad_begin|> + 05:18 [广告-深度转化] <|ad_begin|> + 05:18 [广告-点击] <|ad_begin|> + 05:50 [视频-点赞/长播] <|video_begin|> + 06:40 [广告-深度转化] <|ad_begin|> + 06:40 [广告-点击] <|ad_begin|> + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 08:43 [广告-点击] <|ad_begin|> + 12:31 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> + 21:17 [视频-关注/长播] <|video_begin|> + 22:41 [广告-深度转化] <|ad_begin|> + 22:41 [广告-点击] <|ad_begin|> +【2026-01-31】 + 02:33 [视频-关注/长播] <|video_begin|> + 06:02 [广告-深度转化] <|ad_begin|> + 06:02 [广告-点击] <|ad_begin|> + 07:15 [视频-关注/长播] <|video_begin|> + 07:35 [广告-深度转化] <|ad_begin|> + 07:35 [广告-点击] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> + 12:50 [视频-关注/长播] <|video_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 13:16 [广告-点击] <|ad_begin|> + 14:52 [广告-深度转化] <|ad_begin|> + 14:52 [广告-点击] <|ad_begin|> + 16:31 [视频-关注/长播] <|video_begin|> + 16:36 [视频-关注/长播] <|video_begin|> + 16:47 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:07 [视频-关注/长播] <|video_begin|> + 17:07 [视频-关注/长播] <|video_begin|> + 17:32 [广告-深度转化] <|ad_begin|> + 17:32 [广告-点击] <|ad_begin|> + 17:52 [视频-关注/点赞/长播] <|video_begin|> + 17:56 [视频-关注/长播] <|video_begin|> + 17:58 [视频-关注/长播] <|video_begin|> + 17:59 [视频-关注/长播] <|video_begin|> + 18:50 [视频-关注/点赞/长播] <|video_begin|> + 18:50 [视频-关注/长播] <|video_begin|> + 18:54 [视频-关注/长播] <|video_begin|> + 19:02 [广告-点击] <|ad_begin|> + 19:03 [视频-点赞/长播] <|video_begin|> + 19:04 [视频-关注/点赞/长播] <|video_begin|> + 19:04 [视频-点赞/长播] <|video_begin|> + 19:16 [视频-关注/长播] <|video_begin|> + 19:30 [视频-关注/点赞/长播] <|video_begin|> + 19:30 [视频-关注/点赞/长播] <|video_begin|> + 19:44 [视频-关注/点赞/长播] <|video_begin|> + 19:52 [视频-关注/长播] <|video_begin|> +【2026-02-01】 + 00:15 [广告-点击] <|ad_begin|> + 00:20 [广告-深度转化] <|ad_begin|> + 00:20 [广告-点击] <|ad_begin|> + 01:24 [视频-关注/点赞/长播] <|video_begin|> + 01:27 [视频-点赞/长播] <|video_begin|> + 01:31 [视频-关注/点赞/长播] <|video_begin|> + 01:50 [视频-关注/点赞/长播] <|video_begin|> + 05:57 [视频-点赞/长播] <|video_begin|> + 09:59 [广告-深度转化] <|ad_begin|> + 09:59 [广告-点击] <|ad_begin|> + 10:24 [视频-关注/长播] <|video_begin|> + 10:24 [视频-关注/长播] <|video_begin|> + 10:24 [视频-关注/点赞/长播] <|video_begin|> + 10:30 [视频-关注/长播] <|video_begin|> + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:34 [视频-关注/点赞/长播] <|video_begin|> +【2026-02-02】 + 00:34 [广告-深度转化] <|ad_begin|> + 00:34 [广告-点击] <|ad_begin|> + 09:29 [广告-点击] <|ad_begin|> + 09:29 [视频-关注/点赞/长播] <|video_begin|> + 11:11 [广告-深度转化] <|ad_begin|> + 11:11 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:25 [广告-点击] <|ad_begin|> + 11:56 [广告-点击] <|ad_begin|> + 17:39 [视频-关注/长播] <|video_begin|> +【2026-02-03】 + 00:05 [视频-关注/点赞/长播] <|video_begin|> + 01:18 [视频-关注/长播] <|video_begin|> + 01:18 [视频-关注/点赞/长播] <|video_begin|> + 04:17 [视频-关注/长播] <|video_begin|> + 04:17 [视频-关注/点赞/长播] <|video_begin|> + 04:25 [视频-关注/长播] <|video_begin|> + 04:48 [广告-深度转化] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 05:34 [广告-点击] <|ad_begin|> + 05:35 [视频-关注/转发/点赞/长播] <|video_begin|> + 05:48 [广告-点击] <|ad_begin|> + 05:48 [视频-关注/点赞/长播] <|video_begin|> + 05:48 [视频-点赞/长播] <|video_begin|> + 05:49 [视频-关注/点赞/长播] <|video_begin|> + 05:55 [广告-点击] <|ad_begin|> + 05:59 [视频-关注/长播] <|video_begin|> + 06:09 [视频-关注/长播] <|video_begin|> + 06:09 [视频-关注/点赞/长播] <|video_begin|> + 06:09 [视频-点赞/长播] <|video_begin|> + 16:17 [视频-关注/长播] <|video_begin|> + 16:42 [广告-点击] <|ad_begin|> + 16:43 [视频-关注/长播] <|video_begin|> + 16:43 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:18 [广告-深度转化] <|ad_begin|> + 17:18 [广告-点击] <|ad_begin|> + 19:05 [视频-关注/点赞/长播] <|video_begin|> + 19:24 [视频-关注/长播] <|video_begin|> + 20:18 [广告-深度转化] <|ad_begin|> + 20:18 [广告-点击] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> +【2026-02-04】 + 00:31 [广告-深度转化] <|ad_begin|> + 00:31 [广告-点击] <|ad_begin|> + 00:39 [广告-点击] <|ad_begin|> + 00:48 [广告-点击] <|ad_begin|> + 00:56 [广告-点击] <|ad_begin|> + 00:58 [视频-关注/长播] <|video_begin|> + 00:58 [视频-关注/点赞/长播] <|video_begin|> + 01:26 [广告-深度转化] <|ad_begin|> + 01:26 [广告-点击] <|ad_begin|> + 03:03 [视频-关注/点赞/长播] <|video_begin|> + 03:03 [视频-关注/点赞/长播] <|video_begin|> + 11:19 [广告-点击] <|ad_begin|> + 11:29 [广告-点击] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:16 [视频-关注/点赞/长播] <|video_begin|> + 21:40 [视频-关注/长播] <|video_begin|> + 21:40 [视频-收藏/长播] <|video_begin|> + 21:56 [视频-关注/长播] <|video_begin|> + 21:59 [视频-关注/点赞/长播] <|video_begin|> + 22:06 [广告-点击] <|ad_begin|> + 22:08 [视频-关注/点赞/长播] <|video_begin|> + 22:08 [视频-关注/点赞/长播] <|video_begin|> + 22:25 [视频-点赞/长播] <|video_begin|> + 22:25 [视频-点赞/长播] <|video_begin|> + 23:15 [广告-点击] <|ad_begin|> + 23:26 [视频-长播] <|video_begin|> + 23:31 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:25 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:31 [广告-点击] <|ad_begin|> + 00:35 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + 01:01 [广告-点击] <|ad_begin|> + 01:13 [视频-长播] <|video_begin|> + 01:29 [视频-长播] <|video_begin|> + 01:29 [视频-长播] <|video_begin|> + 03:14 [视频-长播] <|video_begin|> + 04:48 [视频-长播] <|video_begin|> + 04:48 [视频-长播] <|video_begin|> + 04:57 [广告-点击] <|ad_begin|> + 05:30 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:33 [视频-长播] <|video_begin|> + 05:40 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:57 [视频-长播] <|video_begin|> + 05:57 [视频-长播] <|video_begin|> + 09:40 [广告-深度转化] <|ad_begin|> + 09:40 [广告-点击] <|ad_begin|> + 10:12 [广告-深度转化] <|ad_begin|> + 10:12 [广告-点击] <|ad_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-关注/点赞/长播] <|video_begin|> + 12:02 [广告-深度转化] <|ad_begin|> + 12:02 [广告-点击] <|ad_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:36 [广告-点击] <|ad_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:43 [视频-关注/长播] <|video_begin|> + 14:22 [视频-长播] <|video_begin|> + 14:37 [视频-长播] <|video_begin|> + 14:37 [视频-长播] <|video_begin|> + 14:37 [视频-长播] <|video_begin|> + 14:37 [视频-长播] <|video_begin|> + 14:37 [视频-长播] <|video_begin|> + 14:56 [视频-长播] <|video_begin|> + 14:56 [视频-长播] <|video_begin|> + 14:56 [视频-长播] <|video_begin|> + 14:56 [视频-长播] <|video_begin|> + 14:56 [视频-关注/点赞/长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:55 [广告-深度转化] <|ad_begin|> + 15:55 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，依据以上用户交互历史，围绕目标主题，精确提取出所有相关的历史交互信息。 + +主题：从被动接收创业信息到主动探索特定高价值种植项目的兴趣演化 + +输出格式要求：请严格只输出包含 SID 字段的 JSON 数组格式，禁止输出任何其他补充说明或解释性文本。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +["<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-18】 + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + 09:43 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + 04:05 [广告-深度转化] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> +【2025-12-13】 + 03:50 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + 04:03 [广告-深度转化] <|ad_begin|> + 10:35 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 01:09 [广告-深度转化] <|ad_begin|> + 05:39 [广告-深度转化] <|ad_begin|> + 10:54 [广告-深度转化] <|ad_begin|> + 13:29 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + 10:34 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + 10:13 [广告-深度转化] <|ad_begin|> + 10:39 [广告-深度转化] <|ad_begin|> + 13:14 [广告-深度转化] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 15:42 [广告-点击] <|ad_begin|> + 22:42 [广告-深度转化] <|ad_begin|> + 22:42 [广告-点击] <|ad_begin|> +【2025-12-29】 + 12:56 [广告-点击] <|ad_begin|> +【2025-12-30】 + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> + 08:25 [广告-深度转化] <|ad_begin|> + 08:25 [广告-点击] <|ad_begin|> +【2026-01-01】 + 04:15 [广告-深度转化] <|ad_begin|> + 04:15 [广告-点击] <|ad_begin|> + 11:23 [广告-深度转化] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 12:08 [广告-深度转化] <|ad_begin|> + 12:08 [广告-点击] <|ad_begin|> +【2026-01-02】 + 08:19 [广告-深度转化] <|ad_begin|> + 08:19 [广告-点击] <|ad_begin|> + 08:35 [广告-深度转化] <|ad_begin|> + 08:35 [广告-点击] <|ad_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:52 [广告-点击] <|ad_begin|> +【2026-01-07】 + 04:08 [广告-点击] <|ad_begin|> + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> + 15:26 [广告-点击] <|ad_begin|> +【2026-01-10】 + 01:18 [广告-深度转化] <|ad_begin|> + 01:18 [广告-点击] <|ad_begin|> +【2026-01-11】 + 13:15 [广告-深度转化] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> +【2026-01-12】 + 13:16 [广告-点击] <|ad_begin|> +【2026-01-13】 + 13:17 [广告-深度转化] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> + 13:54 [广告-深度转化] <|ad_begin|> + 13:54 [广告-点击] <|ad_begin|> +【2026-01-14】 + 05:03 [广告-深度转化] <|ad_begin|> + 05:03 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> +【2026-01-15】 + 09:56 [广告-深度转化] <|ad_begin|> + 09:56 [广告-点击] <|ad_begin|> +【2026-01-16】 + 04:19 [广告-深度转化] <|ad_begin|> + 04:19 [广告-点击] <|ad_begin|> + 10:35 [广告-深度转化] <|ad_begin|> + 10:35 [广告-点击] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-购买] <|prod_begin|> + 14:13 [广告-深度转化] <|ad_begin|> + 14:13 [广告-点击] <|ad_begin|> +【2026-01-18】 + 04:22 [广告-点击] <|ad_begin|> + 10:26 [广告-深度转化] <|ad_begin|> + 10:26 [广告-点击] <|ad_begin|> + 11:10 [广告-深度转化] <|ad_begin|> + 11:10 [广告-点击] <|ad_begin|> +【2026-01-19】 + 04:11 [广告-点击] <|ad_begin|> + 10:26 [广告-深度转化] <|ad_begin|> + 10:26 [广告-点击] <|ad_begin|> + 12:47 [广告-点击] <|ad_begin|> + 13:09 [广告-深度转化] <|ad_begin|> + 13:09 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:46 [广告-深度转化] <|ad_begin|> + 10:46 [广告-点击] <|ad_begin|> + 12:16 [广告-深度转化] <|ad_begin|> + 12:16 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 10:27 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:50 [视频-点赞/长播] <|video_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 10:24 [广告-点击] <|ad_begin|> + 14:36 [广告-深度转化] <|ad_begin|> + 14:36 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-24】 + 02:49 [广告-深度转化] <|ad_begin|> + 02:49 [广告-点击] <|ad_begin|> + 05:33 [广告-点击] <|ad_begin|> + 06:59 [广告-深度转化] <|ad_begin|> + 06:59 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:11 [广告-深度转化] <|ad_begin|> + 14:11 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + 02:35 [广告-点击] <|ad_begin|> + 04:56 [广告-深度转化] <|ad_begin|> + 04:56 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 10:22 [广告-点击] <|ad_begin|> + 15:03 [广告-深度转化] <|ad_begin|> + 15:03 [广告-点击] <|ad_begin|> + 15:45 [视频-评论/长播] <|video_begin|> + 16:00 [广告-深度转化] <|ad_begin|> + 16:00 [广告-点击] <|ad_begin|> + 16:03 [广告-点击] <|ad_begin|> + 22:23 [视频-点赞/长播] <|video_begin|> +【2026-01-29】 + 10:22 [广告-深度转化] <|ad_begin|> + 10:22 [广告-点击] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> +【2026-01-30】 + 09:08 [广告-点击] <|ad_begin|> + 09:30 [广告-点击] <|ad_begin|> + 11:14 [广告-深度转化] <|ad_begin|> + 11:14 [广告-点击] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 14:52 [广告-深度转化] <|ad_begin|> + 14:52 [广告-点击] <|ad_begin|> + 22:27 [视频-转发/长播] <|video_begin|> +【2026-01-31】 + 04:36 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:14 [广告-深度转化] <|ad_begin|> + 15:14 [广告-点击] <|ad_begin|> +【2026-02-01】 + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 15:15 [广告-深度转化] <|ad_begin|> + 15:15 [广告-点击] <|ad_begin|> +【2026-02-02】 + 12:13 [广告-深度转化] <|ad_begin|> + 12:13 [广告-点击] <|ad_begin|> +【2026-02-03】 + 02:18 [广告-深度转化] <|ad_begin|> + 02:18 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:59 [视频-点赞/长播] <|video_begin|> + 14:08 [广告-点击] <|ad_begin|> +【2026-02-04】 + 10:16 [广告-深度转化] <|ad_begin|> + 10:16 [广告-点击] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + 14:34 [广告-深度转化] <|ad_begin|> + 14:34 [广告-点击] <|ad_begin|> +【2026-02-05】 + 01:55 [广告-深度转化] <|ad_begin|> + 01:55 [广告-点击] <|ad_begin|> + 02:43 [广告-点击] <|ad_begin|> + 02:46 [广告-点击] <|ad_begin|> + 06:12 [视频-转发/长播] <|video_begin|> + 06:39 [视频-长播] <|video_begin|> + 06:41 [视频-长播] <|video_begin|> + 07:04 [视频-长播] <|video_begin|> + 07:06 [视频-长播] <|video_begin|> + 07:11 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:41 [广告-深度转化] <|ad_begin|> + 07:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:44 [视频-长播] <|video_begin|> + 08:48 [视频-长播] <|video_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 10:24 [广告-点击] <|ad_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:58 [视频-长播] <|video_begin|> + 10:58 [视频-长播] <|video_begin|> + 10:58 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-点赞/长播] <|video_begin|> + 11:50 [视频-长播] <|video_begin|> + 14:28 [视频-长播] <|video_begin|> + 14:28 [视频-长播] <|video_begin|> + 14:28 [视频-长播] <|video_begin|> + 14:47 [视频-长播] <|video_begin|> + 14:47 [视频-长播] <|video_begin|> + 14:47 [视频-长播] <|video_begin|> + 14:47 [视频-长播] <|video_begin|> + 14:47 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:16 [视频-长播] <|video_begin|> + 15:16 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 所延伸出的需求，严禁提取任何品类平级、内容重复或仅由天气/促销触发的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请针对下列主题提取行为逻辑链： +主题：从泛化娱乐到具象化工具的决策收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化娱乐到具象化工具的决策收敛", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从泛化娱乐到具象化工具的决策收敛","events":[{"date":"2025-12-15","action":"[广告-深度转化] <|ad_begin|>","logic":"兴趣触发：用户在近一个月内被动接收并深度转化了大量古装、情感、权谋、穿越等题材的短剧广告，表现出对戏剧化、非现实内容的广泛兴趣。"},{"date":"2026-01-17","action":"[商品-购买] <|prod_begin|>","logic":"需求聚焦与行动转化：在临近春节的时间节点（1月17日），用户首次将兴趣从线上虚拟娱乐内容，收敛并转化为对线下、具有强时节性（春节）和明确功能（燃放烟花）的实体工具的购买行为。"},{"date":"2026-01-27","action":"[商品-点击] <|prod_begin|>","logic":"认知深化与参数筛选：在首次购买后，用户并未停止探索，而是开始关注更专业的参数，如“6毫米壁厚”、“无缝钢管材质”、“二次封底”，这表明其认知已从满足基本“能用”的需求，跃迁至对工具的安全性、耐用性和专业性的深度考量，产生了显著的认知增量。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-07-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-27】 + --:-- [直播-关注] <|living_begin|> +【2025-08-01】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> +【2025-09-06】 + --:-- [直播-关注] <|living_begin|> +【2025-09-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-21】 + --:-- [直播-关注] <|living_begin|> +【2025-09-22】 + --:-- [直播-关注] <|living_begin|> +【2025-10-20】 + --:-- [直播-关注] <|living_begin|> +【2025-10-23】 + --:-- [直播-关注] <|living_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-09】 + --:-- [直播-关注] <|living_begin|> +【2025-11-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + 05:06 [广告-深度转化] <|ad_begin|> + 07:51 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + 03:26 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + --:-- [直播-首次打赏] <|living_begin|> + 03:40 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-19】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-20】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-26】 + 03:30 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-01】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-02】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-06】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + 01:09 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-14】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-15】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + 13:30 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-17】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:04 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-24】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:04 [视频-长播] <|video_begin|> + 10:42 [视频-长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:42 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:43 [视频-长播] <|video_begin|> + 12:07 [广告-深度转化] <|ad_begin|> + 12:20 [广告-深度转化] <|ad_begin|> + 22:41 [视频-长播] <|video_begin|> + 22:41 [视频-长播] <|video_begin|> + 22:53 [视频-长播] <|video_begin|> + 22:53 [视频-长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 01:44 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:06 [广告-深度转化] <|ad_begin|> +【2026-01-29】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:34 [视频-长播] <|video_begin|> + 23:46 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:21 [视频-长播] <|video_begin|> + 10:42 [广告-深度转化] <|ad_begin|> + 10:50 [视频-长播] <|video_begin|> + 11:01 [视频-长播] <|video_begin|> + 11:09 [视频-长播] <|video_begin|> + 11:09 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:25 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 11:58 [视频-长播] <|video_begin|> + 22:49 [视频-长播] <|video_begin|> + 22:49 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-打赏] <|living_begin|> + 10:17 [视频-长播] <|video_begin|> + 22:33 [视频-长播] <|video_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:33 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:09 [视频-长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:39 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 13:13 [视频-长播] <|video_begin|> + 13:13 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 21:42 [视频-长播] <|video_begin|> + 21:42 [视频-长播] <|video_begin|> + 21:42 [视频-长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 12:17 [视频-长播] <|video_begin|> + 18:46 [视频-长播] <|video_begin|> + 19:05 [视频-长播] <|video_begin|> + 19:41 [视频-长播] <|video_begin|> +【2026-02-04】 + 01:12 [视频-长播] <|video_begin|> + 01:12 [视频-长播] <|video_begin|> + 01:27 [广告-点击] <|ad_begin|> + 01:29 [广告-点击] <|ad_begin|> + 01:30 [广告-点击] <|ad_begin|> + 01:32 [广告-点击] <|ad_begin|> + 01:32 [广告-点击] <|ad_begin|> + 01:33 [广告-点击] <|ad_begin|> + 01:35 [广告-点击] <|ad_begin|> + 03:07 [广告-点击] <|ad_begin|> + 05:46 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:15 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:18 [广告-点击] <|ad_begin|> + 11:21 [广告-点击] <|ad_begin|> + 11:28 [广告-点击] <|ad_begin|> + 11:38 [广告-点击] <|ad_begin|> + 11:49 [广告-点击] <|ad_begin|> + 11:53 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 12:17 [广告-点击] <|ad_begin|> + 12:18 [广告-点击] <|ad_begin|> + 19:13 [广告-点击] <|ad_begin|> + 19:21 [广告-点击] <|ad_begin|> + 19:36 [广告-点击] <|ad_begin|> + 19:38 [广告-点击] <|ad_begin|> + 19:43 [视频-长播] <|video_begin|> + 19:50 [视频-长播] <|video_begin|> + 19:59 [广告-点击] <|ad_begin|> + 20:00 [广告-点击] <|ad_begin|> + 20:02 [广告-点击] <|ad_begin|> + 20:30 [广告-点击] <|ad_begin|> + 20:34 [广告-点击] <|ad_begin|> + 21:11 [广告-点击] <|ad_begin|> + 21:12 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:47 [广告-点击] <|ad_begin|> + 00:51 [广告-点击] <|ad_begin|> + 00:54 [广告-点击] <|ad_begin|> + 05:29 [广告-点击] <|ad_begin|> + 06:06 [广告-点击] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> + 06:13 [广告-点击] <|ad_begin|> + 06:16 [广告-点击] <|ad_begin|> + 06:17 [广告-点击] <|ad_begin|> + 06:18 [广告-点击] <|ad_begin|> + 06:21 [广告-点击] <|ad_begin|> + 06:27 [广告-点击] <|ad_begin|> + 07:25 [广告-点击] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 07:38 [广告-点击] <|ad_begin|> + 07:39 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:42 [广告-点击] <|ad_begin|> + 10:46 [广告-点击] <|ad_begin|> + 10:47 [广告-点击] <|ad_begin|> + 10:50 [广告-点击] <|ad_begin|> + 10:51 [广告-点击] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 10:54 [广告-点击] <|ad_begin|> + 10:56 [广告-点击] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 10:58 [广告-点击] <|ad_begin|> + 11:00 [广告-点击] <|ad_begin|> + 11:01 [广告-点击] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> + 11:10 [广告-点击] <|ad_begin|> + 11:15 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 11:49 [广告-点击] <|ad_begin|> + 11:49 [广告-点击] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 11:53 [广告-点击] <|ad_begin|> + 11:53 [广告-点击] <|ad_begin|> + 12:04 [广告-点击] <|ad_begin|> + 13:32 [广告-深度转化] <|ad_begin|> + 13:32 [广告-点击] <|ad_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家的身份，基于以上交互历史，针对给定主题筛选并提取出全部相关的历史交互行为。 + +主题：从被动娱乐到主动解决健康问题的需求演化 + +输出格式要求：请严格只输出包含 SID 字段的 JSON 数组格式，禁止输出任何其他补充说明或解释性文本。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant + + +["<|video_begin|>","<|prod_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-04-10】 + --:-- [商品-购买] <|prod_begin|> +【2023-06-06】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-14】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-12】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-07】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-08】 + --:-- [商品-点击] <|prod_begin|> + 06:44 [广告-点击] <|ad_begin|> +【2025-12-09】 + 05:46 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-13】 + 07:55 [广告-点击] <|ad_begin|> +【2025-12-18】 + 04:50 [广告-深度转化] <|ad_begin|> + 04:50 [广告-点击] <|ad_begin|> +【2025-12-21】 + 11:39 [广告-点击] <|ad_begin|> +【2025-12-22】 + 04:43 [广告-点击] <|ad_begin|> + 06:17 [广告-深度转化] <|ad_begin|> + 06:17 [广告-点击] <|ad_begin|> +【2025-12-24】 + 05:56 [广告-深度转化] <|ad_begin|> + 05:56 [广告-点击] <|ad_begin|> +【2025-12-25】 + 04:25 [广告-深度转化] <|ad_begin|> + 04:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> +【2025-12-28】 + 03:26 [广告-深度转化] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:46 [广告-点击] <|ad_begin|> +【2025-12-29】 + 01:13 [广告-深度转化] <|ad_begin|> + 01:13 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 11:04 [广告-深度转化] <|ad_begin|> + 11:04 [广告-点击] <|ad_begin|> + 20:54 [广告-点击] <|ad_begin|> +【2025-12-30】 + 02:23 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:16 [广告-深度转化] <|ad_begin|> + 07:16 [广告-点击] <|ad_begin|> +【2025-12-31】 + 01:41 [广告-深度转化] <|ad_begin|> + 01:41 [广告-点击] <|ad_begin|> + 07:35 [广告-深度转化] <|ad_begin|> + 07:35 [广告-点击] <|ad_begin|> +【2026-01-01】 + 03:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:40 [广告-深度转化] <|ad_begin|> + 10:40 [广告-点击] <|ad_begin|> +【2026-01-04】 + 05:41 [广告-深度转化] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:26 [广告-点击] <|ad_begin|> +【2026-01-05】 + 05:32 [广告-深度转化] <|ad_begin|> + 05:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:44 [广告-深度转化] <|ad_begin|> + 07:44 [广告-点击] <|ad_begin|> + 11:53 [广告-深度转化] <|ad_begin|> + 11:53 [广告-点击] <|ad_begin|> + 16:10 [广告-点击] <|ad_begin|> + 16:12 [广告-点击] <|ad_begin|> + 16:59 [广告-点击] <|ad_begin|> + 17:00 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + 12:12 [广告-深度转化] <|ad_begin|> + 12:12 [广告-点击] <|ad_begin|> +【2026-01-07】 + 05:06 [广告-深度转化] <|ad_begin|> + 05:06 [广告-点击] <|ad_begin|> +【2026-01-09】 + 09:22 [广告-深度转化] <|ad_begin|> + 09:22 [广告-点击] <|ad_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + 10:42 [广告-深度转化] <|ad_begin|> + 10:42 [广告-点击] <|ad_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> + 11:57 [广告-点击] <|ad_begin|> + 12:28 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:40 [广告-深度转化] <|ad_begin|> + 08:40 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:29 [广告-点击] <|ad_begin|> +【2026-01-18】 + 09:58 [广告-深度转化] <|ad_begin|> + 09:58 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 18:47 [广告-点击] <|ad_begin|> +【2026-01-20】 + 05:40 [广告-深度转化] <|ad_begin|> + 05:40 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 08:13 [视频-评论/长播] <|video_begin|> +【2026-01-23】 + 05:46 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-25】 + 04:18 [广告-点击] <|ad_begin|> + 04:23 [视频-评论/长播] <|video_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + 09:25 [广告-深度转化] <|ad_begin|> + 09:25 [广告-点击] <|ad_begin|> +【2026-01-28】 + 02:35 [广告-点击] <|ad_begin|> +【2026-01-29】 + --:-- [直播-关注] <|living_begin|> +【2026-01-30】 + 08:30 [视频-长播] <|video_begin|> + 08:30 [视频-长播] <|video_begin|> + 09:05 [视频-长播] <|video_begin|> + 09:11 [广告-深度转化] <|ad_begin|> + 09:11 [广告-点击] <|ad_begin|> + 09:28 [广告-深度转化] <|ad_begin|> + 09:28 [广告-点击] <|ad_begin|> + 11:13 [广告-点击] <|ad_begin|> +【2026-01-31】 + 04:12 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 05:09 [视频-长播] <|video_begin|> + 05:19 [广告-深度转化] <|ad_begin|> + 05:19 [广告-点击] <|ad_begin|> + 11:15 [广告-深度转化] <|ad_begin|> + 11:15 [广告-点击] <|ad_begin|> +【2026-02-01】 + 11:18 [视频-长播] <|video_begin|> + 11:19 [视频-长播] <|video_begin|> +【2026-02-02】 + 00:36 [视频-长播] <|video_begin|> + 00:45 [广告-深度转化] <|ad_begin|> + 00:45 [广告-点击] <|ad_begin|> + 06:37 [视频-长播] <|video_begin|> + 06:37 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 07:07 [广告-深度转化] <|ad_begin|> + 07:07 [广告-点击] <|ad_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:57 [视频-长播] <|video_begin|> + 10:57 [视频-长播] <|video_begin|> + 10:58 [视频-长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:05 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> +【2026-02-03】 + 03:14 [视频-长播] <|video_begin|> + 06:05 [广告-深度转化] <|ad_begin|> + 06:05 [广告-点击] <|ad_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:56 [视频-长播] <|video_begin|> + 11:07 [广告-深度转化] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> +【2026-02-04】 + 01:34 [视频-长播] <|video_begin|> + 01:50 [视频-长播] <|video_begin|> + 01:53 [视频-长播] <|video_begin|> + 01:55 [视频-长播] <|video_begin|> + 01:59 [视频-长播] <|video_begin|> + 01:59 [视频-长播] <|video_begin|> + 01:59 [视频-长播] <|video_begin|> + 02:06 [视频-长播] <|video_begin|> + 02:13 [视频-长播] <|video_begin|> + 02:16 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:02 [广告-深度转化] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> + 11:05 [视频-长播] <|video_begin|> + 11:05 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:40 [视频-长播] <|video_begin|> + 00:43 [视频-长播] <|video_begin|> + 03:13 [视频-长播] <|video_begin|> + 03:25 [视频-长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:33 [视频-长播] <|video_begin|> + 03:41 [视频-长播] <|video_begin|> + 03:41 [视频-长播] <|video_begin|> + 03:52 [视频-长播] <|video_begin|> + 03:54 [视频-长播] <|video_begin|> + 03:57 [视频-长播] <|video_begin|> + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，依据以上用户交互历史，围绕目标主题，精确提取出所有相关的历史交互信息。 + +主题：从泛化养生到工具化实践的兴趣聚焦 + +输出格式要求：输出必须且仅为包含 SID 的 JSON 数组，绝对不要输出任何额外的解释、提示或格式化外的文字。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +["<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> +<|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, 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+我首先对用户的整体行为进行了梳理。从2025年7月到2026年初，我注意到这位用户的消费行为非常丰富，涵盖了食品零食、服饰鞋包、美妆护肤、家居日用以及一些健身器材等多个品类。这表明她是一位乐于尝试和消费的年轻女性。在内容消费方面，尤其是在2026年1月底到2月初，她密集地观看了大量视频，内容五花八门，包括搞笑短剧、明星八卦、社会新闻、情感故事、影视剪辑和生活技巧等，显示出她兴趣广泛，但似乎没有特别聚焦的领域。 + +我的分析重点放在了寻找一个从“看”到“做”的转变点上。在浏览2026年2月3日的行为时，我发现了一个非常清晰的信号。在当天上午08:20，用户观看并评论了一个展示手工编织围巾的视频 <|video_begin|>。这个视频强调了手工制品所蕴含的“情感价值”和“人情味”，这是一种情感层面的触动，而不仅仅是展示商品。我推测，这种情感化的内容很可能激发了用户自己动手制作的念头。 + +紧接着，我发现用户的行为立刻印证了我的推测。就在观看完视频之后，同一天内，她开始密集地点击与“手工编织”相关的商品。例如，她点击了标题中包含“手工长绒毛围巾diy自制编织心意礼物”的商品 <|prod_begin|>，以及多款以“蜡笔小新”为主题的DIY围巾材料包。这些点击行为中的关键词，如“diy”、“手工编织”、“心意礼物”，与她刚刚观看的视频内容高度契合。这表明她的兴趣已经从被动的观看，迅速转化为主动的、有明确目的的商品调研。她不再只是欣赏成品，而是在寻找能够让她亲身实践的工具和材料。 + +最终，在经过一番浏览和比较之后，用户在当天就完成了购买决策。她购买了一款名为“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包”的商品 <|prod_begin|>。这个购买行为是整个兴趣链条的关键闭环，标志着她完成了从被动的信息接收者到主动的实践者的身份转变。整个过程从看到视频激发兴趣，到搜索和比较DIY材料，再到最终下单购买，逻辑清晰，时间紧凑，构成了一条完整的兴趣唤醒与实践路径。 + +综合以上分析，我认为用户的行为清晰地展示了从被动接收信息到主动实践的手工兴趣唤醒过程。这个过程始于观看一个强调情感价值的手工编织视频 <|video_begin|>，这激发了她的制作兴趣；随后，她立即开始浏览具体的DIY围巾材料包商品，例如点击了“手工长绒毛围巾diy自制编织心意礼物” <|prod_begin|>，将模糊的兴趣转化为明确的行动意图；最终，通过购买“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包” <|prod_begin|>，完成了从认知到行为的转化。因此，未来向她推荐更多DIY手工制作类的内容或商品，有较大概率被吸引。 +{"logic_chain": {"name": "从被动接收信息到主动实践的手工兴趣唤醒", "events": [{"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣触发：被动观看到手工编织围巾的视频，内容强调“情感价值”和“人情味”，可能激发了用户的制作兴趣。"}, {"date": "2026-02-03", "action": "[商品-点击] <|prod_begin|>", "logic": "意图确认与调研：观看视频后，用户立即开始浏览具体的“DIY材料包”商品，关键词“手工”、“diy自制”、“心意礼物”与视频内容高度关联，表明兴趣已转化为具体行动意图。"}, {"date": "2026-02-03", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在浏览了多种DIY围巾材料包后，用户最终完成购买，实现了从“被动观看”到“主动实践”的认知与行为跃迁。"}]}}<|im_end|> +["<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> + + +{"logic_chain":{"name":"从泛化娱乐到具象化工具的决策收敛","events":[{"date":"2025-12-15","action":"[广告-深度转化] <|ad_begin|>","logic":"兴趣触发：用户在近一个月内被动接收并深度转化了大量古装、情感、权谋、穿越等题材的短剧广告，表现出对戏剧化、非现实内容的广泛兴趣。"},{"date":"2026-01-17","action":"[商品-购买] <|prod_begin|>","logic":"需求聚焦与行动转化：在临近春节的时间节点（1月17日），用户首次将兴趣从线上虚拟娱乐内容，收敛并转化为对线下、具有强时节性（春节）和明确功能（燃放烟花）的实体工具的购买行为。"},{"date":"2026-01-27","action":"[商品-点击] <|prod_begin|>","logic":"认知深化与参数筛选：在首次购买后，用户并未停止探索，而是开始关注更专业的参数，如“6毫米壁厚”、“无缝钢管材质”、“二次封底”，这表明其认知已从满足基本“能用”的需求，跃迁至对工具的安全性、耐用性和专业性的深度考量，产生了显著的认知增量。"}]}}<|im_end|> + + +["<|video_begin|>","<|prod_begin|>"]<|im_end|> +["<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 03:27:50,016 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 03:27:50,017 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-15 03:27:50] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 03:27:50] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 03:27:50,353 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-15 03:27:50,353 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 03:27:50,498 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-15 03:27:50,775 >> Generate config GenerationConfig { + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-15 03:27:50,775 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-15 03:27:50] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 03:27:50] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 03:27:50] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 03:27:50] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 03:27:50] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 03:27:50,907 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 03:27:50,907 >> Num examples = 1,562 +[INFO|trainer.py:1471] 2026-07-15 03:27:50,907 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-15 03:27:50,907 >> Num update steps per epoch = 391 +[INFO|trainer.py:1473] 2026-07-15 03:27:50,907 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 03:27:50,907 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 03:27:50,907 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 03:27:50,907 >> Total optimization steps = 313 +[INFO|trainer.py:1479] 2026-07-15 03:27:50,908 >> Number of trainable parameters = 801,433,600 + 0%| | 0/313 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 0%| | 1/313 [00:08<42:59, 8.27s/it] 1%| | 2/313 [00:16<43:45, 8.44s/it] 1%| | 3/313 [00:24<41:24, 8.01s/it] 1%|▏ | 4/313 [00:31<39:02, 7.58s/it] 2%|▏ | 5/313 [00:39<39:38, 7.72s/it] 2%|▏ | 6/313 [00:46<38:33, 7.54s/it] 2%|▏ | 7/313 [00:54<38:44, 7.60s/it] 3%|▎ | 8/313 [01:01<38:24, 7.55s/it] 3%|▎ | 9/313 [01:09<38:13, 7.54s/it] 3%|▎ | 10/313 [01:16<38:30, 7.63s/it] {'loss': '1.856', 'grad_norm': '5.312', 'learning_rate': '5.4e-07', 'epoch': '0.02561'} + 3%|▎ | 10/313 [01:16<38:30, 7.63s/it] 4%|▎ | 11/313 [01:24<38:55, 7.73s/it] 4%|▍ | 12/313 [01:32<38:55, 7.76s/it] 4%|▍ | 13/313 [01:40<39:33, 7.91s/it] 4%|▍ | 14/313 [01:48<38:39, 7.76s/it] 5%|▍ | 15/313 [01:56<38:54, 7.83s/it] 5%|▌ | 16/313 [02:04<38:56, 7.87s/it] 5%|▌ | 17/313 [02:11<38:21, 7.77s/it] 6%|▌ | 18/313 [02:19<37:58, 7.72s/it] 6%|▌ | 19/313 [02:27<38:20, 7.83s/it] 6%|▋ | 20/313 [02:34<36:32, 7.48s/it] {'loss': '1.952', 'grad_norm': '6.125', 'learning_rate': '5.987e-07', 'epoch': '0.05122'} + 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'learning_rate': '5.865e-07', 'epoch': '0.1024'} + 13%|█▎ | 40/313 [05:03<34:14, 7.53s/it] 13%|█▎ | 41/313 [05:11<35:02, 7.73s/it] 13%|█▎ | 42/313 [05:19<34:59, 7.75s/it] 14%|█▎ | 43/313 [05:27<34:36, 7.69s/it] 14%|█▍ | 44/313 [05:36<36:11, 8.07s/it] 14%|█▍ | 45/313 [05:43<35:24, 7.93s/it] 15%|█▍ | 46/313 [05:50<33:28, 7.52s/it] 15%|█▌ | 47/313 [05:58<34:07, 7.70s/it] 15%|█▌ | 48/313 [06:05<33:37, 7.62s/it] 16%|█▌ | 49/313 [06:13<33:56, 7.72s/it] 16%|█▌ | 50/313 [06:20<32:40, 7.45s/it] {'loss': '1.733', 'grad_norm': '11.56', 'learning_rate': '5.758e-07', 'epoch': '0.128'} + 16%|█▌ | 50/313 [06:20<32:40, 7.45s/it] 16%|█▋ | 51/313 [06:28<33:11, 7.60s/it] 17%|█▋ | 52/313 [06:35<31:57, 7.35s/it] 17%|█▋ | 53/313 [06:43<32:55, 7.60s/it] 17%|█▋ | 54/313 [06:50<31:28, 7.29s/it] 18%|█▊ | 55/313 [06:57<31:27, 7.32s/it] 18%|█▊ | 56/313 [07:06<32:42, 7.64s/it] 18%|█▊ | 57/313 [07:14<33:12, 7.78s/it] 19%|█▊ | 58/313 [07:21<32:39, 7.68s/it] 19%|█▉ | 59/313 [07:30<33:51, 8.00s/it] 19%|█▉ | 60/313 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8.09s/it] {'loss': '1.891', 'grad_norm': '39', 'learning_rate': '6.366e-08', 'epoch': '0.6402'} + 80%|███████▉ | 250/313 [32:16<08:29, 8.09s/it] 80%|████████ | 251/313 [32:24<08:22, 8.10s/it] 81%|████████ | 252/313 [32:32<08:20, 8.20s/it] 81%|████████ | 253/313 [32:40<07:58, 7.98s/it] 81%|████████ | 254/313 [32:47<07:42, 7.83s/it] 81%|████████▏ | 255/313 [32:55<07:35, 7.85s/it] 82%|████████▏ | 256/313 [33:03<07:34, 7.97s/it] 82%|████████▏ | 257/313 [33:11<07:19, 7.84s/it] 82%|████████▏ | 258/313 [33:18<07:01, 7.66s/it] 83%|████████▎ | 259/313 [33:26<06:57, 7.74s/it] 83%|████████▎ | 260/313 [33:35<07:03, 8.00s/it] {'loss': '1.836', 'grad_norm': '11.56', 'learning_rate': '4.581e-08', 'epoch': '0.6658'} + 83%|████████▎ | 260/313 [33:35<07:03, 8.00s/it] 83%|████████▎ | 261/313 [33:43<07:07, 8.22s/it] 84%|████████▎ | 262/313 [33:51<06:57, 8.19s/it] 84%|████████▍ | 263/313 [33:58<06:22, 7.66s/it] 84%|████████▍ | 264/313 [34:06<06:24, 7.84s/it] 85%|████████▍ | 265/313 [34:14<06:20, 7.94s/it] 85%|████████▍ | 266/313 [34:23<06:20, 8.11s/it] 85%|████████▌ | 267/313 [34:30<06:00, 7.83s/it] 86%|████████▌ | 268/313 [34:38<05:51, 7.82s/it] 86%|████████▌ | 269/313 [34:45<05:34, 7.61s/it] 86%|████████▋ | 270/313 [34:53<05:28, 7.64s/it] {'loss': '1.758', 'grad_norm': '4.031', 'learning_rate': '3.068e-08', 'epoch': '0.6914'} + 86%|████████▋ | 270/313 [34:53<05:28, 7.64s/it] 87%|████████▋ | 271/313 [34:59<05:12, 7.44s/it] 87%|████████▋ | 272/313 [35:07<05:07, 7.51s/it] 87%|████████▋ | 273/313 [35:15<05:08, 7.71s/it] 88%|████████▊ | 274/313 [35:24<05:06, 7.85s/it] 88%|████████▊ | 275/313 [35:31<04:57, 7.83s/it] 88%|████████▊ | 276/313 [35:39<04:48, 7.80s/it] 88%|████████▊ | 277/313 [35:48<04:48, 8.01s/it] 89%|████████▉ | 278/313 [35:56<04:48, 8.23s/it] 89%|████████▉ | 279/313 [36:04<04:33, 8.03s/it] 89%|████████▉ | 280/313 [36:12<04:27, 8.12s/it] {'loss': '1.621', 'grad_norm': '4.531', 'learning_rate': '1.845e-08', 'epoch': '0.717'} + 89%|████████▉ | 280/313 [36:12<04:27, 8.12s/it] 90%|████████▉ | 281/313 [36:20<04:18, 8.08s/it] 90%|█████████ | 282/313 [36:28<04:06, 7.94s/it] 90%|█████████ | 283/313 [36:35<03:55, 7.84s/it] 91%|█████████ | 284/313 [36:43<03:44, 7.73s/it] 91%|█████████ | 285/313 [36:50<03:29, 7.48s/it] 91%|█████████▏| 286/313 [36:58<03:28, 7.71s/it] 92%|█████████▏| 287/313 [37:06<03:24, 7.86s/it] 92%|█████████▏| 288/313 [37:15<03:20, 8.02s/it] 92%|█████████▏| 289/313 [37:21<03:03, 7.63s/it] 93%|█████████▎| 290/313 [37:29<02:54, 7.58s/it] {'loss': '1.824', 'grad_norm': '24.12', 'learning_rate': '9.24e-09', 'epoch': '0.7426'} + 93%|█████████▎| 290/313 [37:29<02:54, 7.58s/it] 93%|█████████▎| 291/313 [37:37<02:48, 7.68s/it] 93%|█████████▎| 292/313 [37:44<02:40, 7.66s/it] 94%|█████████▎| 293/313 [37:52<02:33, 7.66s/it] 94%|█████████▍| 294/313 [38:00<02:24, 7.62s/it] 94%|█████████▍| 295/313 [38:06<02:10, 7.24s/it] 95%|█████████▍| 296/313 [38:13<02:00, 7.10s/it] 95%|█████████▍| 297/313 [38:20<01:53, 7.06s/it] 95%|█████████▌| 298/313 [38:28<01:50, 7.40s/it] 96%|█████████▌| 299/313 [38:36<01:48, 7.72s/it] 96%|█████████▌| 300/313 [38:44<01:39, 7.67s/it] {'loss': '1.738', 'grad_norm': '6.062', 'learning_rate': '3.155e-09', 'epoch': '0.7682'} + 96%|█████████▌| 300/313 [38:44<01:39, 7.67s/it] 96%|█████████▌| 301/313 [38:52<01:33, 7.80s/it] 96%|█████████▋| 302/313 [39:00<01:25, 7.80s/it] 97%|█████████▋| 303/313 [39:07<01:17, 7.72s/it] 97%|█████████▋| 304/313 [39:16<01:10, 7.88s/it] 97%|█████████▋| 305/313 [39:24<01:03, 7.92s/it] 98%|█████████▊| 306/313 [39:31<00:54, 7.84s/it] 98%|█████████▊| 307/313 [39:39<00:47, 7.88s/it] 98%|█████████▊| 308/313 [39:46<00:38, 7.61s/it] 99%|█████████▊| 309/313 [39:54<00:31, 7.76s/it] 99%|█████████▉| 310/313 [40:01<00:22, 7.59s/it] {'loss': '1.801', 'grad_norm': '9.188', 'learning_rate': '2.58e-10', 'epoch': '0.7939'} + 99%|█████████▉| 310/313 [40:01<00:22, 7.59s/it] 99%|█████████▉| 311/313 [40:10<00:15, 7.93s/it] 100%|█████████▉| 312/313 [40:18<00:08, 8.03s/it] 100%|██████████| 313/313 [40:27<00:00, 8.06s/it][INFO|trainer.py:1823] 2026-07-15 04:08:17,991 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '2427', 'train_samples_per_second': '0.515', 'train_steps_per_second': '0.129', 'train_loss': '1.822', 'epoch': '0.8015'} + 100%|██████████| 313/313 [40:27<00:00, 8.06s/it] 100%|██████████| 313/313 [40:27<00:00, 7.75s/it] +[INFO|trainer.py:3815] 2026-07-15 04:08:18,001 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary +[INFO|configuration_utils.py:533] 2026-07-15 04:08:18,002 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/config.json +[INFO|configuration_utils.py:816] 2026-07-15 04:08:18,002 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 04:08:18,951 >> chat template saved in /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 04:08:18,973 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/tokenizer_config.json +***** train metrics ***** + epoch = 0.8015 + total_flos = 138327489GF + train_loss = 1.8218 + train_runtime = 0:40:27.09 + train_samples_per_second = 0.515 + train_steps_per_second = 0.129 +Figure saved at: /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/training_loss.png +[WARNING|2026-07-15 04:08:19] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 04:08:19] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 04:08:19,072 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} +[2026-07-15 04:08:19] stage 2/2: /home/ll/llm4rec/experiments/configs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage2.yaml +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-15 04:08:22] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:769] 2026-07-15 04:08:22,457 >> loading configuration file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:08:22,459 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-15 04:08:23,131 >> loading configuration file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:08:23,131 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-15 04:08:23,131 >> loading configuration file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:08:23,132 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-15 04:08:23] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v39_curriculum_r3.jsonl... +Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. + Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 22425 examples [00:00, 217919.87 examples/s] Generating train split: 53023 examples [00:00, 248754.74 examples/s] Generating train split: 70000 examples [00:00, 252714.15 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/70000 [00:00user +【用户交互历史】： +【2025-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> 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<|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> +【2025-12-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + 10:18 [广告-深度转化] <|ad_begin|> + 10:18 [广告-深度转化] <|ad_begin|> + 10:21 [广告-深度转化] <|ad_begin|> + 10:35 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 03:11 [广告-深度转化] <|ad_begin|> + 03:13 [广告-深度转化] <|ad_begin|> + 03:14 [广告-深度转化] <|ad_begin|> + 03:15 [广告-深度转化] <|ad_begin|> + 03:18 [广告-深度转化] <|ad_begin|> + 03:18 [广告-深度转化] <|ad_begin|> + 07:31 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:43 [广告-深度转化] <|ad_begin|> + 06:43 [广告-深度转化] <|ad_begin|> + 07:45 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [商品-购买] <|prod_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 12:10 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:50 [广告-深度转化] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 07:30 [广告-深度转化] <|ad_begin|> + 07:30 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> 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04:20 [广告-深度转化] <|ad_begin|> + 04:20 [广告-点击] <|ad_begin|> + 04:21 [广告-点击] <|ad_begin|> + 04:24 [广告-深度转化] <|ad_begin|> + 04:24 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 08:48 [视频-关注/转发/长播] <|video_begin|> + 09:50 [广告-点击] <|ad_begin|> + 11:03 [广告-深度转化] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> +【2026-01-29】 + 01:32 [视频-点赞/长播] <|video_begin|> + 02:00 [视频-点赞/长播] <|video_begin|> + 10:38 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 06:58 [视频-点赞/长播] <|video_begin|> +【2026-01-31】 + 01:08 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:18 [广告-深度转化] <|ad_begin|> + 04:18 [广告-点击] <|ad_begin|> + 04:36 [广告-点击] <|ad_begin|> + 06:09 [广告-点击] <|ad_begin|> + 23:31 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 00:35 [广告-点击] <|ad_begin|> + 00:38 [广告-点击] <|ad_begin|> + 00:40 [广告-点击] <|ad_begin|> + 02:51 [广告-深度转化] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 04:18 [广告-点击] <|ad_begin|> + 04:43 [广告-点击] <|ad_begin|> + 04:45 [广告-点击] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 09:12 [广告-点击] <|ad_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 23:57 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 00:14 [广告-点击] <|ad_begin|> + 01:33 [视频-点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:28 [视频-长播] <|video_begin|> + 23:58 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:05 [视频-长播] <|video_begin|> + 00:33 [视频-长播] <|video_begin|> + 00:33 [视频-点赞/长播] <|video_begin|> + 00:34 [视频-长播] <|video_begin|> + 01:21 [视频-长播] <|video_begin|> + 01:53 [视频-转发/长播] <|video_begin|> + 02:00 [视频-长播] <|video_begin|> + 02:00 [视频-长播] <|video_begin|> + 03:19 [视频-收藏/长播] <|video_begin|> + 03:27 [视频-收藏/长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:37 [视频-长播] <|video_begin|> + 03:42 [视频-长播] <|video_begin|> + 04:15 [视频-点赞/长播] <|video_begin|> + 04:38 [广告-深度转化] <|ad_begin|> + 04:38 [广告-点击] <|ad_begin|> + 04:58 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 07:22 [广告-深度转化] <|ad_begin|> + 07:22 [广告-点击] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 07:24 [视频-长播] <|video_begin|> + 07:24 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:45 [视频-长播] <|video_begin|> + 23:46 [视频-长播] <|video_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> +【2026-02-04】 + 00:09 [视频-长播] <|video_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:36 [广告-点击] <|ad_begin|> + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + 03:44 [视频-长播] <|video_begin|> + 04:24 [广告-点击] <|ad_begin|> + 05:06 [视频-长播] <|video_begin|> + 05:09 [广告-深度转化] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 05:10 [广告-深度转化] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:43 [视频-收藏/点赞/长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 08:59 [广告-点击] <|ad_begin|> + 09:00 [视频-点赞/长播] <|video_begin|> + 09:07 [视频-关注/点赞/长播] <|video_begin|> + 09:15 [视频-长播] <|video_begin|> + 09:15 [视频-收藏/点赞/长播] <|video_begin|> + 09:16 [视频-关注/点赞/长播] <|video_begin|> + 23:32 [视频-长播] <|video_begin|> + 23:33 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:10 [视频-长播] <|video_begin|> + 00:10 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-长播] <|video_begin|> + 00:16 [视频-长播] <|video_begin|> + 01:08 [视频-长播] <|video_begin|> + 01:08 [视频-长播] <|video_begin|> + 01:11 [视频-长播] <|video_begin|> + 01:29 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:11 [广告-点击] <|ad_begin|> + 03:31 [视频-收藏/长播] <|video_begin|> + 03:35 [视频-点赞/长播] <|video_begin|> + 03:39 [视频-收藏/点赞/长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:47 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 06:17 [广告-点击] <|ad_begin|> + 06:18 [广告-深度转化] <|ad_begin|> + 06:18 [广告-点击] <|ad_begin|> + 06:19 [广告-深度转化] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 06:20 [广告-深度转化] <|ad_begin|> + 06:20 [广告-点击] <|ad_begin|> + 06:20 [广告-点击] <|ad_begin|> + 08:11 [视频-评论/收藏/长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:33 [视频-长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 08:59 [广告-点击] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家和数据格式化专家，请基于以上用户交互历史，围绕指定主题提取具备行为递进关系与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +按时间顺序提取 5 步以内的行为链，要求后项交互B必须是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是因为 A 才产生的延伸需求，严禁提取任何品类平级、内容重复或仅因天气/促销产生的凑单并列行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请围绕以下主题提取行为逻辑链： +主题：从泛化健康焦虑到聚焦特定问题的解决方案探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化健康焦虑到聚焦特定问题的解决方案探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化健康焦虑到聚焦特定问题的解决方案探索","events":[{"date":"2025-06-26","action":"[商品-购买] <|prod_begin|>","logic":"问题触发与初步尝试：首次购买明确指向“便秘”问题的产品，表明用户开始正视该健康问题，并尝试通过天然食疗方式解决。"},{"date":"2025-06-29","action":"[商品-购买] <|prod_begin|>","logic":"认知升级与路径修正：在购买食疗产品后仅3天，用户迅速转向购买具有明确药理作用的“通便”胶囊。这表明食疗方案可能效果不佳或不及时，促使用户寻求更直接、更强效的药物解决方案，完成了从“调理”到“治疗”的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-03-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-22】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-29】 + --:-- [直播-打赏] <|living_begin|> +【2025-07-29】 + --:-- [直播-打赏] <|living_begin|> +【2025-08-01】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-03】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-07】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-08】 + --:-- [直播-打赏] <|living_begin|> +【2025-08-13】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-08-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-27】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-02】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-06】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-08】 + --:-- [直播-关注] <|living_begin|> +【2025-09-09】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-13】 + --:-- [直播-关注] <|living_begin|> +【2025-09-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-15】 + --:-- [直播-关注] <|living_begin|> +【2025-09-18】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-22】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-28】 + --:-- [直播-关注] <|living_begin|> +【2025-10-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-02】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-06】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-16】 + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-26】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-10-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-02】 + --:-- [直播-关注] <|living_begin|> +【2025-11-03】 + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-18】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-26】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-01】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-02】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-08】 + 20:45 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-11】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-21】 + 15:22 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 04:13 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + --:-- [直播-关注] <|living_begin|> +【2025-12-28】 + 10:59 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> +【2026-01-02】 + --:-- [直播-首次打赏] <|living_begin|> + 03:18 [广告-深度转化] <|ad_begin|> + 03:19 [广告-深度转化] <|ad_begin|> + 03:20 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 04:28 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 04:15 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> +【2026-01-07】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-10】 + --:-- [直播-首次打赏] <|living_begin|> + 11:31 [广告-深度转化] <|ad_begin|> + 11:33 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 07:05 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-23】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-24】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 00:42 [视频-长播] <|video_begin|> + 16:02 [视频-长播] <|video_begin|> + 20:01 [视频-长播] <|video_begin|> +【2026-01-26】 + 00:01 [视频-长播] <|video_begin|> + 00:01 [视频-长播] <|video_begin|> + 00:01 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + --:-- [直播-首次打赏] <|living_begin|> + 00:47 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 15:36 [视频-长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 05:46 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-29】 + --:-- [直播-首次打赏] <|living_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 00:47 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:43 [视频-长播] <|video_begin|> + 15:36 [视频-长播] <|video_begin|> + 15:40 [视频-长播] <|video_begin|> + 21:02 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 03:12 [视频-长播] <|video_begin|> + 03:52 [广告-点击] <|ad_begin|> + 06:48 [广告-点击] <|ad_begin|> + 12:44 [广告-点击] <|ad_begin|> + 16:06 [广告-点击] <|ad_begin|> + 16:09 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 16:12 [广告-点击] <|ad_begin|> + 16:42 [广告-点击] <|ad_begin|> + 16:50 [广告-点击] <|ad_begin|> + 20:32 [广告-点击] <|ad_begin|> + 23:44 [广告-点击] <|ad_begin|> + 23:46 [广告-点击] <|ad_begin|> +【2026-02-01】 + 03:45 [广告-点击] <|ad_begin|> + 03:58 [广告-点击] <|ad_begin|> + 04:01 [广告-点击] <|ad_begin|> + 04:02 [广告-点击] <|ad_begin|> + 04:40 [广告-点击] <|ad_begin|> + 06:33 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 09:19 [视频-收藏/长播] <|video_begin|> + 09:58 [广告-点击] <|ad_begin|> + 10:51 [广告-点击] <|ad_begin|> + 16:03 [广告-点击] <|ad_begin|> + 16:08 [广告-点击] <|ad_begin|> + 16:38 [广告-点击] <|ad_begin|> + 16:55 [广告-点击] <|ad_begin|> + 23:31 [广告-点击] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 00:49 [广告-点击] <|ad_begin|> + 04:42 [视频-长播] <|video_begin|> + 04:42 [视频-长播] <|video_begin|> + 05:09 [广告-点击] <|ad_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:15 [视频-长播] <|video_begin|> + 06:15 [视频-长播] <|video_begin|> + 06:15 [视频-长播] <|video_begin|> + 06:15 [视频-长播] <|video_begin|> + 06:46 [视频-长播] <|video_begin|> + 11:04 [广告-点击] <|ad_begin|> + 11:05 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 16:07 [广告-点击] <|ad_begin|> + 16:10 [广告-点击] <|ad_begin|> + 16:12 [广告-点击] <|ad_begin|> + 16:16 [广告-点击] <|ad_begin|> + 16:53 [广告-点击] <|ad_begin|> + 17:25 [广告-点击] <|ad_begin|> + 17:26 [广告-点击] <|ad_begin|> + 20:19 [广告-点击] <|ad_begin|> + 23:06 [广告-点击] <|ad_begin|> + 23:07 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:17 [广告-点击] <|ad_begin|> + 23:21 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:29 [广告-点击] <|ad_begin|> + 05:26 [视频-长播] <|video_begin|> + 05:26 [视频-长播] <|video_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:07 [广告-点击] <|ad_begin|> + 16:09 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 16:18 [广告-点击] <|ad_begin|> + 16:46 [广告-点击] <|ad_begin|> + 17:14 [广告-点击] <|ad_begin|> + 17:24 [广告-点击] <|ad_begin|> + 17:26 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:24 [广告-点击] <|ad_begin|> + 23:26 [广告-点击] <|ad_begin|> + 23:46 [广告-点击] <|ad_begin|> + 23:47 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 00:00 [视频-长播] <|video_begin|> + 00:00 [视频-长播] <|video_begin|> + 00:00 [视频-长播] <|video_begin|> + 03:51 [广告-点击] <|ad_begin|> + 05:26 [广告-点击] <|ad_begin|> + 08:34 [广告-点击] <|ad_begin|> + 16:09 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 16:18 [广告-点击] <|ad_begin|> + 16:31 [广告-点击] <|ad_begin|> + 17:16 [广告-点击] <|ad_begin|> + 20:13 [广告-点击] <|ad_begin|> + 20:17 [广告-点击] <|ad_begin|> + 23:37 [广告-点击] <|ad_begin|> + 23:45 [广告-点击] <|ad_begin|> + 23:46 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:02 [视频-长播] <|video_begin|> + 00:02 [视频-长播] <|video_begin|> + 08:09 [视频-长播] <|video_begin|> + 08:09 [视频-长播] <|video_begin|> + +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家的身份，基于以上交互历史，针对给定主题筛选并提取出全部相关的历史交互行为。 + +主题：从被动信息接收到主动探索国际地缘政治与军事话题的认知深化 + +输出格式要求：请严格只输出包含 SID 字段的 JSON 数组格式，禁止输出任何其他补充说明或解释性文本。/no_think<|im_end|> +<|im_start|>assistant + + +["<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-06-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-03】 + --:-- [直播-关注] <|living_begin|> +【2025-01-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-04】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-14】 + --:-- [直播-关注] <|living_begin|> +【2025-03-22】 + --:-- [直播-关注] <|living_begin|> +【2025-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-02】 + --:-- [直播-关注] <|living_begin|> +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> +【2025-04-10】 + --:-- [直播-关注] <|living_begin|> +【2025-04-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-12】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-03】 + --:-- [直播-关注] <|living_begin|> +【2025-05-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [直播-关注] <|living_begin|> +【2025-06-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-06-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-21】 + --:-- [直播-关注] <|living_begin|> +【2025-08-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-10】 + --:-- [直播-关注] <|living_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-16】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-21】 + --:-- [直播-关注] <|living_begin|> +【2025-09-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-03】 + --:-- [直播-关注] <|living_begin|> +【2025-10-09】 + --:-- [直播-关注] <|living_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-12】 + --:-- [直播-关注] <|living_begin|> +【2025-12-13】 + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + 13:26 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + 12:26 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 12:58 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 03:52 [广告-深度转化] <|ad_begin|> + 09:29 [广告-深度转化] <|ad_begin|> + 12:52 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 15:53 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + 11:37 [广告-深度转化] <|ad_begin|> + 12:29 [广告-深度转化] <|ad_begin|> + 13:51 [广告-深度转化] <|ad_begin|> + 14:20 [广告-深度转化] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:39 [广告-深度转化] <|ad_begin|> + 14:50 [广告-深度转化] <|ad_begin|> + 14:59 [广告-深度转化] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:45 [广告-深度转化] <|ad_begin|> + 16:09 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + 05:08 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + 02:36 [广告-深度转化] <|ad_begin|> + 06:50 [广告-深度转化] <|ad_begin|> + 06:59 [广告-深度转化] <|ad_begin|> + 08:58 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 01:26 [广告-深度转化] <|ad_begin|> + 14:36 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 03:41 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 08:13 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> + 01:38 [广告-深度转化] <|ad_begin|> + 09:18 [广告-深度转化] <|ad_begin|> + 14:24 [广告-深度转化] <|ad_begin|> + 14:26 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 09:00 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:26 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + --:-- [商品-购买] <|prod_begin|> + 13:41 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 12:28 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 09:17 [广告-深度转化] <|ad_begin|> + 10:27 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 06:38 [广告-深度转化] <|ad_begin|> + 08:27 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 10:52 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 13:18 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 13:01 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:12 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 14:18 [广告-点击] <|ad_begin|> + 14:18 [广告-深度转化] <|ad_begin|> + 14:18 [广告-点击] <|ad_begin|> + 15:23 [广告-深度转化] <|ad_begin|> + 15:23 [广告-点击] <|ad_begin|> + 21:14 [广告-点击] <|ad_begin|> + 21:47 [广告-点击] <|ad_begin|> + 21:50 [广告-深度转化] <|ad_begin|> + 21:50 [广告-点击] <|ad_begin|> + 21:50 [广告-点击] <|ad_begin|> +【2026-01-17】 + 09:28 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> + 13:29 [广告-点击] <|ad_begin|> + 13:29 [广告-点击] <|ad_begin|> + 13:30 [广告-点击] <|ad_begin|> + 13:33 [广告-点击] <|ad_begin|> + 13:59 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + 09:38 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:37 [广告-点击] <|ad_begin|> +【2026-01-19】 + 12:32 [广告-点击] <|ad_begin|> + 14:00 [广告-深度转化] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + 09:04 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:33 [广告-点击] <|ad_begin|> + 15:01 [广告-点击] <|ad_begin|> + 19:21 [广告-深度转化] <|ad_begin|> + 19:21 [广告-点击] <|ad_begin|> +【2026-01-21】 + 03:55 [广告-点击] <|ad_begin|> + 06:08 [广告-深度转化] <|ad_begin|> + 06:08 [广告-点击] <|ad_begin|> + 06:13 [广告-深度转化] <|ad_begin|> + 06:13 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 01:44 [广告-点击] <|ad_begin|> + 03:23 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:39 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:35 [视频-收藏/长播] <|video_begin|> + 12:24 [广告-深度转化] <|ad_begin|> + 12:24 [广告-点击] <|ad_begin|> +【2026-01-24】 + 01:02 [广告-点击] <|ad_begin|> + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:53 [广告-深度转化] <|ad_begin|> + 13:53 [广告-点击] <|ad_begin|> + 14:15 [广告-深度转化] <|ad_begin|> + 14:15 [广告-点击] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> +【2026-01-25】 + 06:28 [广告-深度转化] <|ad_begin|> + 06:28 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:48 [广告-深度转化] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-01-26】 + 06:44 [广告-点击] <|ad_begin|> + 07:15 [视频-收藏/长播] <|video_begin|> + 07:36 [广告-点击] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:08 [广告-深度转化] <|ad_begin|> + 13:08 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> + 13:19 [广告-深度转化] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + 13:47 [广告-点击] <|ad_begin|> + 13:59 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + 13:14 [广告-点击] <|ad_begin|> +【2026-01-29】 + 04:18 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:27 [广告-深度转化] <|ad_begin|> + 06:27 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:27 [视频-收藏/长播] <|video_begin|> +【2026-01-30】 + 03:46 [广告-深度转化] <|ad_begin|> + 03:46 [广告-点击] <|ad_begin|> + 04:59 [广告-点击] <|ad_begin|> + 06:54 [广告-点击] <|ad_begin|> + 07:04 [广告-点击] <|ad_begin|> + 07:27 [视频-点赞/长播] <|video_begin|> + 07:29 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:14 [广告-点击] <|ad_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:34 [广告-点击] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> + 12:53 [广告-点击] <|ad_begin|> +【2026-01-31】 + 09:31 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + 05:59 [广告-点击] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:20 [广告-点击] <|ad_begin|> +【2026-02-04】 + 01:48 [广告-深度转化] <|ad_begin|> + 01:48 [广告-点击] <|ad_begin|> + 03:13 [视频-长播] <|video_begin|> + 03:13 [视频-长播] <|video_begin|> + 03:21 [视频-长播] <|video_begin|> + 03:21 [视频-长播] <|video_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:20 [视频-长播] <|video_begin|> + 06:20 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:45 [视频-长播] <|video_begin|> + 07:49 [视频-长播] <|video_begin|> + 11:02 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:46 [视频-浏览] <|video_begin|> + 12:11 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 13:14 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:14 [视频-长播] <|video_begin|> + 14:44 [视频-长播] <|video_begin|> +【2026-02-05】 + 03:19 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 06:02 [视频-长播] <|video_begin|> + 06:18 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 07:18 [视频-长播] <|video_begin|> + 07:18 [视频-点赞/长播] <|video_begin|> + 07:29 [视频-长播] <|video_begin|> + 07:29 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:50 [视频-长播] <|video_begin|> + 08:38 [视频-长播] <|video_begin|> + 08:38 [视频-长播] <|video_begin|> + 08:38 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 09:12 [视频-长播] <|video_begin|> + 10:31 [视频-长播] <|video_begin|> + 10:31 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:31 [视频-长播] <|video_begin|> + 11:38 [视频-长播] <|video_begin|> + 11:38 [视频-长播] <|video_begin|> + 11:38 [视频-长播] <|video_begin|> + 12:02 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:10 [视频-长播] <|video_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 12:25 [广告-点击] <|ad_begin|> + 12:58 [视频-长播] <|video_begin|> + 12:58 [视频-长播] <|video_begin|> + +角色任务：你是极端严苛的用户行为数据挖掘与数据格式化专家，请结合上述用户交互历史，面向给定主题，全面提取出所有具备关联性的历史交互数据。 + +主题：从被动娱乐到主动求知的认知转向 + +输出格式要求：请仅以包含 SID 的 JSON 数组形式返回结果，切勿输出任何额外的解释说明或无关字符。/no_think<|im_end|> +<|im_start|>assistant + + +["<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|video_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-02】 + --:-- [直播-关注] <|living_begin|> +【2025-01-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-01-19】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> +【2025-08-10】 + --:-- [直播-关注] <|living_begin|> +【2025-08-17】 + --:-- [直播-关注] <|living_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 12:24 [广告-深度转化] <|ad_begin|> + 22:39 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 12:04 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + --:-- [商品-购买] <|prod_begin|> + 08:25 [广告-深度转化] <|ad_begin|> + 10:13 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:12 [广告-深度转化] <|ad_begin|> + 02:12 [广告-点击] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 14:24 [广告-深度转化] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> + 07:01 [广告-深度转化] <|ad_begin|> + 07:01 [广告-点击] <|ad_begin|> + 07:21 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 11:31 [广告-点击] <|ad_begin|> + 13:39 [广告-深度转化] <|ad_begin|> + 13:39 [广告-点击] <|ad_begin|> +【2026-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [商品-购买] <|prod_begin|> + 12:18 [广告-点击] <|ad_begin|> + 12:53 [广告-点击] <|ad_begin|> + 13:00 [广告-深度转化] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> +【2026-01-03】 + 00:27 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + --:-- [商品-购买] <|prod_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 12:25 [广告-点击] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> + 15:55 [广告-深度转化] <|ad_begin|> + 15:55 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:10 [广告-深度转化] <|ad_begin|> + 11:10 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> + 03:08 [广告-深度转化] <|ad_begin|> + 03:08 [广告-点击] <|ad_begin|> +【2026-01-17】 + 05:34 [广告-深度转化] <|ad_begin|> + 05:34 [广告-点击] <|ad_begin|> + 14:43 [广告-点击] <|ad_begin|> +【2026-01-19】 + 02:08 [广告-点击] <|ad_begin|> + 03:48 [广告-深度转化] <|ad_begin|> + 03:48 [广告-点击] <|ad_begin|> + 10:38 [广告-点击] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 13:51 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:29 [广告-点击] <|ad_begin|> + 20:09 [广告-深度转化] <|ad_begin|> + 20:09 [广告-点击] <|ad_begin|> +【2026-01-21】 + 15:13 [广告-深度转化] <|ad_begin|> + 15:13 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [商品-购买] <|prod_begin|> + 04:05 [视频-关注/长播] <|video_begin|> + 13:18 [广告-深度转化] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> +【2026-01-23】 + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> + 12:03 [视频-关注/长播] <|video_begin|> +【2026-01-24】 + 10:30 [广告-深度转化] <|ad_begin|> + 10:30 [广告-点击] <|ad_begin|> + 21:59 [广告-深度转化] <|ad_begin|> + 21:59 [广告-点击] <|ad_begin|> +【2026-01-25】 + 01:43 [视频-关注/长播] <|video_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 13:29 [广告-深度转化] <|ad_begin|> + 13:29 [广告-点击] <|ad_begin|> +【2026-01-26】 + 03:04 [广告-点击] <|ad_begin|> + 07:05 [广告-深度转化] <|ad_begin|> + 07:05 [广告-点击] <|ad_begin|> + 08:43 [视频-转发/长播] <|video_begin|> +【2026-01-27】 + 01:38 [广告-深度转化] <|ad_begin|> + 01:38 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 06:11 [广告-深度转化] <|ad_begin|> + 06:11 [广告-点击] <|ad_begin|> + 08:53 [广告-深度转化] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + 14:01 [广告-深度转化] <|ad_begin|> + 14:01 [广告-点击] <|ad_begin|> + 23:05 [视频-关注/长播] <|video_begin|> +【2026-01-28】 + 07:12 [广告-深度转化] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> +【2026-01-29】 + 03:07 [广告-深度转化] <|ad_begin|> + 03:07 [广告-点击] <|ad_begin|> + 04:51 [广告-深度转化] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 06:09 [广告-深度转化] <|ad_begin|> + 06:09 [广告-点击] <|ad_begin|> + 14:13 [广告-深度转化] <|ad_begin|> + 14:13 [广告-点击] <|ad_begin|> +【2026-01-30】 + 07:16 [广告-深度转化] <|ad_begin|> + 07:16 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:45 [广告-深度转化] <|ad_begin|> + 02:45 [广告-点击] <|ad_begin|> + 05:16 [广告-深度转化] <|ad_begin|> + 05:16 [广告-点击] <|ad_begin|> + 10:54 [广告-深度转化] <|ad_begin|> + 10:54 [广告-点击] <|ad_begin|> + 11:38 [广告-深度转化] <|ad_begin|> + 11:38 [广告-点击] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 14:49 [广告-点击] <|ad_begin|> + 22:59 [广告-深度转化] <|ad_begin|> + 22:59 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:22 [广告-点击] <|ad_begin|> + 03:46 [广告-深度转化] <|ad_begin|> + 03:46 [广告-点击] <|ad_begin|> + 06:26 [广告-点击] <|ad_begin|> +【2026-02-02】 + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:09 [广告-深度转化] <|ad_begin|> + 04:09 [广告-点击] <|ad_begin|> + 06:24 [广告-深度转化] <|ad_begin|> + 06:24 [广告-点击] <|ad_begin|> + 11:11 [广告-深度转化] <|ad_begin|> + 11:11 [广告-点击] <|ad_begin|> +【2026-02-03】 + 01:13 [广告-深度转化] <|ad_begin|> + 01:13 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:35 [广告-深度转化] <|ad_begin|> + 04:35 [广告-点击] <|ad_begin|> + 04:45 [广告-点击] <|ad_begin|> + 06:04 [广告-深度转化] <|ad_begin|> + 06:04 [广告-点击] <|ad_begin|> + 08:52 [广告-深度转化] <|ad_begin|> + 08:52 [广告-点击] <|ad_begin|> + 11:02 [广告-深度转化] <|ad_begin|> + 11:02 [广告-点击] <|ad_begin|> + 23:23 [广告-深度转化] <|ad_begin|> + 23:23 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + 03:25 [视频-长播] <|video_begin|> + 03:52 [视频-长播] <|video_begin|> + 03:52 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:43 [视频-点赞/长播] <|video_begin|> + 06:54 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:48 [广告-深度转化] <|ad_begin|> + 07:48 [广告-点击] <|ad_begin|> + 11:58 [视频-长播] <|video_begin|> + 11:58 [视频-长播] <|video_begin|> + 11:58 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 13:44 [视频-长播] <|video_begin|> + 13:52 [视频-长播] <|video_begin|> + 13:52 [视频-长播] <|video_begin|> + 13:52 [视频-长播] <|video_begin|> + 13:52 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:27 [视频-长播] <|video_begin|> + 14:27 [视频-长播] <|video_begin|> + 14:27 [视频-长播] <|video_begin|> + 14:32 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> +【2026-02-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:14 [视频-长播] <|video_begin|> + 03:38 [视频-长播] <|video_begin|> + 03:38 [视频-长播] <|video_begin|> + 03:46 [视频-长播] <|video_begin|> + 04:33 [视频-长播] <|video_begin|> + 04:33 [视频-长播] <|video_begin|> + 04:35 [视频-长播] <|video_begin|> + 07:16 [视频-长播] <|video_begin|> + 07:48 [视频-长播] <|video_begin|> + 07:59 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:24 [广告-深度转化] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:22 [视频-长播] <|video_begin|> + 09:57 [广告-深度转化] <|ad_begin|> + 09:57 [广告-点击] <|ad_begin|> + 11:31 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +按时间顺序提取 5 步以内的行为链，要求后项交互B必须是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是因为 A 才产生的延伸需求，严禁提取任何品类平级、内容重复或仅因天气/促销产生的凑单并列行为。 +1.场景驱动协同：交互 B 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请面向以下主题提取行为逻辑链： +主题：从泛化需求到精准解决：围绕墙面修复问题的决策收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化需求到精准解决：围绕墙面修复问题的决策收敛", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化需求到精准解决：围绕墙面修复问题的决策收敛","events":[{"date":"2026-02-01","action":"[商品-点击] <|prod_begin|>","logic":"问题触发与方案探索：首次点击“墙面自喷漆补墙膏”，明确了“墙面修复”这一具体需求，并开始探索市场上的解决方案。"},{"date":"2026-02-01","action":"[商品-点击] <|prod_begin|>","logic":"参数对比与需求细化：从宽泛的“补墙膏”转向关注具体参数的产品，如“1600ml加量”、“5合1”、“净味”、“修复发霉发黄”，显示用户在对比中认知升级，开始筛选具备特定功能和更高性价比的产品。"},{"date":"2026-02-01","action":"[商品-购买] <|prod_begin|>","logic":"决策闭环：在密集对比多款产品后，最终购买了明确标注“19.9两瓶”、“300ml大容量”的商品，完成了从问题识别、方案对比到最终购买决策的完整、收敛的行为链条。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-14】 + --:-- [直播-关注] <|living_begin|> +【2025-01-22】 + --:-- [直播-关注] <|living_begin|> +【2025-01-23】 + --:-- [直播-关注] <|living_begin|> +【2025-02-03】 + --:-- [直播-关注] <|living_begin|> +【2025-02-18】 + --:-- [直播-关注] <|living_begin|> +【2025-02-28】 + --:-- [直播-关注] <|living_begin|> +【2025-03-15】 + --:-- [直播-关注] <|living_begin|> +【2025-03-18】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-03-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [直播-关注] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-11】 + --:-- [直播-关注] <|living_begin|> +【2025-06-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> +【2025-09-18】 + --:-- [直播-关注] <|living_begin|> +【2025-09-21】 + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [直播-关注] <|living_begin|> +【2025-10-22】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-10-30】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-07】 + --:-- [直播-关注] <|living_begin|> +【2025-11-09】 + --:-- [直播-关注] <|living_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 12:55 [广告-点击] <|ad_begin|> + 13:10 [广告-深度转化] <|ad_begin|> + 13:10 [广告-点击] <|ad_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> + 14:01 [广告-深度转化] <|ad_begin|> + 14:01 [广告-点击] <|ad_begin|> + 21:18 [广告-点击] <|ad_begin|> + 21:39 [广告-点击] <|ad_begin|> +【2025-12-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-12】 + 12:12 [广告-点击] <|ad_begin|> +【2025-12-13】 + 16:19 [广告-点击] <|ad_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-17】 + 04:46 [广告-点击] <|ad_begin|> +【2025-12-20】 + 20:24 [广告-点击] <|ad_begin|> + 20:54 [广告-点击] <|ad_begin|> + 20:55 [广告-点击] <|ad_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:53 [广告-点击] <|ad_begin|> +【2025-12-26】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:58 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-加购] <|prod_begin|> +【2025-12-28】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2025-12-29】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2025-12-30】 + --:-- [商品-加购] <|prod_begin|> +【2025-12-31】 + 21:34 [广告-点击] <|ad_begin|> + 21:42 [广告-深度转化] <|ad_begin|> + 21:42 [广告-点击] <|ad_begin|> + 22:53 [广告-点击] <|ad_begin|> +【2026-01-01】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:39 [广告-点击] <|ad_begin|> +【2026-01-06】 + 07:14 [广告-点击] <|ad_begin|> + 12:51 [广告-深度转化] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + 12:53 [广告-深度转化] <|ad_begin|> + 12:53 [广告-点击] <|ad_begin|> +【2026-01-07】 + 04:32 [广告-深度转化] <|ad_begin|> + 04:32 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:31 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:50 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [商品-购买] <|prod_begin|> + 20:43 [广告-点击] <|ad_begin|> +【2026-01-10】 + --:-- [商品-购买] <|prod_begin|> + 12:21 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + 05:57 [广告-深度转化] <|ad_begin|> + 05:57 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + 15:49 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + --:-- [商品-购买] <|prod_begin|> + 09:35 [广告-点击] <|ad_begin|> +【2026-01-23】 + 04:29 [视频-收藏/长播] <|video_begin|> + 04:29 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:39 [视频-长播] <|video_begin|> + 15:39 [视频-关注/长播] <|video_begin|> +【2026-01-24】 + 04:12 [视频-收藏/长播] <|video_begin|> + 04:12 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 16:10 [视频-长播] <|video_begin|> +【2026-01-25】 + 15:51 [广告-点击] <|ad_begin|> + 16:13 [视频-长播] <|video_begin|> +【2026-01-26】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:49 [视频-长播] <|video_begin|> + 23:50 [视频-长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:20 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 07:30 [视频-长播] <|video_begin|> + 08:07 [视频-长播] <|video_begin|> + 13:57 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:02 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:55 [视频-长播] <|video_begin|> + 23:55 [视频-长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:16 [广告-点击] <|ad_begin|> +【2026-02-04】 + 01:14 [视频-关注/长播] <|video_begin|> + 04:22 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:30 [视频-长播] <|video_begin|> + 12:30 [视频-长播] <|video_begin|> +【2026-02-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家的身份，基于以上交互历史，针对给定主题筛选并提取出全部相关的历史交互行为。 + +主题：从文玩入门到专业化工具升级的消费演化 + +输出格式要求：输出必须且仅为包含 SID 的 JSON 数组，绝对不要输出任何额外的解释、提示或格式化外的文字。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +["<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> +<|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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"<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|video_begin|>", "<|video_begin|>"]<|im_end|> +{"logic_chain":{"name":"从泛化需求到精准解决：围绕墙面修复问题的决策收敛","events":[{"date":"2026-02-01","action":"[商品-点击] <|prod_begin|>","logic":"问题触发与方案探索：首次点击“墙面自喷漆补墙膏”，明确了“墙面修复”这一具体需求，并开始探索市场上的解决方案。"},{"date":"2026-02-01","action":"[商品-点击] <|prod_begin|>","logic":"参数对比与需求细化：从宽泛的“补墙膏”转向关注具体参数的产品，如“1600ml加量”、“5合1”、“净味”、“修复发霉发黄”，显示用户在对比中认知升级，开始筛选具备特定功能和更高性价比的产品。"},{"date":"2026-02-01","action":"[商品-购买] <|prod_begin|>","logic":"决策闭环：在密集对比多款产品后，最终购买了明确标注“19.9两瓶”、“300ml大容量”的商品，完成了从问题识别、方案对比到最终购买决策的完整、收敛的行为链条。"}]}}<|im_end|> +["<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 04:08:47,700 >> loading configuration file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:08:47,701 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-15 04:08:47] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 04:08:47] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 04:08:48,019 >> loading weights file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-15 04:08:48,019 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 04:08:48,167 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-15 04:08:48,446 >> Generate config GenerationConfig { + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-15 04:08:48,446 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/.v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2_stage1_temporary. +[INFO|2026-07-15 04:08:48] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 04:08:48] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 04:08:48] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 04:08:48] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 04:08:48] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 04:08:48,575 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 04:08:48,575 >> Num examples = 1,634 +[INFO|trainer.py:1471] 2026-07-15 04:08:48,575 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-15 04:08:48,575 >> Num update steps per epoch = 409 +[INFO|trainer.py:1473] 2026-07-15 04:08:48,575 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 04:08:48,575 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 04:08:48,575 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 04:08:48,575 >> Total optimization steps = 328 +[INFO|trainer.py:1479] 2026-07-15 04:08:48,575 >> Number of trainable parameters = 801,433,600 + 0%| | 0/328 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 0%| | 1/328 [00:08<44:07, 8.09s/it] 1%| | 2/328 [00:15<42:47, 7.87s/it] 1%| | 3/328 [00:24<45:26, 8.39s/it] 1%| | 4/328 [00:32<43:13, 8.00s/it] 2%|▏ | 5/328 [00:38<40:27, 7.52s/it] 2%|▏ | 6/328 [00:46<41:15, 7.69s/it] 2%|▏ | 7/328 [00:54<41:26, 7.75s/it] 2%|▏ | 8/328 [01:02<40:56, 7.68s/it] 3%|▎ | 9/328 [01:09<40:26, 7.61s/it] 3%|▎ | 10/328 [01:17<41:00, 7.74s/it] {'loss': '2.033', 'grad_norm': '6', 'learning_rate': '4.5e-07', 'epoch': '0.02448'} + 3%|▎ | 10/328 [01:17<41:00, 7.74s/it] 3%|▎ | 11/328 [01:25<41:00, 7.76s/it] 4%|▎ | 12/328 [01:33<41:53, 7.95s/it] 4%|▍ | 13/328 [01:41<41:06, 7.83s/it] 4%|▍ | 14/328 [01:49<40:34, 7.75s/it] 5%|▍ | 15/328 [01:57<41:13, 7.90s/it] 5%|▍ | 16/328 [02:05<41:36, 8.00s/it] 5%|▌ | 17/328 [02:13<40:46, 7.87s/it] 5%|▌ | 18/328 [02:20<39:43, 7.69s/it] 6%|▌ | 19/328 [02:28<39:59, 7.76s/it] 6%|▌ | 20/328 [02:34<37:51, 7.38s/it] {'loss': '2.124', 'grad_norm': '4.938', 'learning_rate': '4.99e-07', 'epoch': '0.04896'} + 6%|▌ | 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'4.898e-07', 'epoch': '0.09792'} + 12%|█▏ | 40/328 [05:08<36:32, 7.61s/it] 12%|█▎ | 41/328 [05:16<36:10, 7.56s/it] 13%|█▎ | 42/328 [05:22<34:46, 7.29s/it] 13%|█▎ | 43/328 [05:30<34:43, 7.31s/it] 13%|█▎ | 44/328 [05:37<34:58, 7.39s/it] 14%|█▎ | 45/328 [05:46<36:11, 7.67s/it] 14%|█▍ | 46/328 [05:54<36:42, 7.81s/it] 14%|█▍ | 47/328 [06:01<36:25, 7.78s/it] 15%|█▍ | 48/328 [06:09<36:14, 7.77s/it] 15%|█▍ | 49/328 [06:17<36:04, 7.76s/it] 15%|█▌ | 50/328 [06:25<36:57, 7.98s/it] {'loss': '2.168', 'grad_norm': '11.69', 'learning_rate': '4.817e-07', 'epoch': '0.1224'} + 15%|█▌ | 50/328 [06:25<36:57, 7.98s/it] 16%|█▌ | 51/328 [06:31<34:09, 7.40s/it] 16%|█▌ | 52/328 [06:39<34:41, 7.54s/it] 16%|█▌ | 53/328 [06:46<32:50, 7.17s/it] 16%|█▋ | 54/328 [06:51<30:12, 6.62s/it] 17%|█▋ | 55/328 [06:58<30:59, 6.81s/it] 17%|█▋ | 56/328 [07:06<32:53, 7.25s/it] 17%|█▋ | 57/328 [07:14<33:28, 7.41s/it] 18%|█▊ | 58/328 [07:22<33:33, 7.46s/it] 18%|█▊ | 59/328 [07:31<35:19, 7.88s/it] 18%|█▊ | 60/328 [07:37<33:12, 7.44s/it] {'loss': '2.077', 'grad_norm': '27.5', 'learning_rate': '4.713e-07', 'epoch': '0.1469'} + 18%|█▊ | 60/328 [07:37<33:12, 7.44s/it] 19%|█▊ | 61/328 [07:45<33:08, 7.45s/it] 19%|█▉ | 62/328 [07:52<32:50, 7.41s/it] 19%|█▉ | 63/328 [07:59<32:28, 7.35s/it] 20%|█▉ | 64/328 [08:07<32:45, 7.45s/it] 20%|█▉ | 65/328 [08:13<30:49, 7.03s/it] 20%|██ | 66/328 [08:21<32:05, 7.35s/it] 20%|██ | 67/328 [08:29<32:41, 7.52s/it] 21%|██ | 68/328 [08:36<32:15, 7.44s/it] 21%|██ | 69/328 [08:42<30:46, 7.13s/it] 21%|██▏ | 70/328 [08:50<30:47, 7.16s/it] {'loss': '2.266', 'grad_norm': '14.69', 'learning_rate': '4.587e-07', 'epoch': '0.1714'} + 21%|██▏ | 70/328 [08:50<30:47, 7.16s/it] 22%|██▏ | 71/328 [08:58<32:04, 7.49s/it] 22%|██▏ | 72/328 [09:06<32:56, 7.72s/it] 22%|██▏ | 73/328 [09:14<32:48, 7.72s/it] 23%|██▎ | 74/328 [09:21<32:06, 7.58s/it] 23%|██▎ | 75/328 [09:29<32:33, 7.72s/it] 23%|██▎ | 76/328 [09:36<30:39, 7.30s/it] 23%|██▎ | 77/328 [09:43<30:40, 7.33s/it] 24%|██▍ | 78/328 [09:49<29:33, 7.10s/it] 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[37:56<03:32, 7.60s/it] {'loss': '2.057', 'grad_norm': '10.62', 'learning_rate': '1.019e-08', 'epoch': '0.7344'} + 91%|█████████▏| 300/328 [37:56<03:32, 7.60s/it] 92%|█████████▏| 301/328 [38:03<03:18, 7.37s/it] 92%|█████████▏| 302/328 [38:10<03:12, 7.41s/it] 92%|█████████▏| 303/328 [38:18<03:08, 7.54s/it] 93%|█████████▎| 304/328 [38:27<03:08, 7.85s/it] 93%|█████████▎| 305/328 [38:34<02:53, 7.53s/it] 93%|█████████▎| 306/328 [38:41<02:47, 7.61s/it] 94%|█████████▎| 307/328 [38:49<02:37, 7.50s/it] 94%|█████████▍| 308/328 [38:56<02:29, 7.47s/it] 94%|█████████▍| 309/328 [39:04<02:23, 7.57s/it] 95%|█████████▍| 310/328 [39:11<02:12, 7.39s/it] {'loss': '2.139', 'grad_norm': '13.75', 'learning_rate': '4.391e-09', 'epoch': '0.7589'} + 95%|█████████▍| 310/328 [39:11<02:12, 7.39s/it] 95%|█████████▍| 311/328 [39:18<02:04, 7.35s/it] 95%|█████████▌| 312/328 [39:26<02:01, 7.59s/it] 95%|█████████▌| 313/328 [39:33<01:51, 7.47s/it] 96%|█████████▌| 314/328 [39:42<01:47, 7.68s/it] 96%|█████████▌| 315/328 [39:49<01:40, 7.71s/it] 96%|█████████▋| 316/328 [39:56<01:30, 7.55s/it] 97%|█████████▋| 317/328 [40:04<01:24, 7.65s/it] 97%|█████████▋| 318/328 [40:12<01:15, 7.53s/it] 97%|█████████▋| 319/328 [40:19<01:08, 7.61s/it] 98%|█████████▊| 320/328 [40:27<01:01, 7.68s/it] {'loss': '2.129', 'grad_norm': '14.81', 'learning_rate': '9.875e-10', 'epoch': '0.7834'} + 98%|█████████▊| 320/328 [40:27<01:01, 7.68s/it] 98%|█████████▊| 321/328 [40:35<00:54, 7.76s/it] 98%|█████████▊| 322/328 [40:43<00:46, 7.82s/it] 98%|█████████▊| 323/328 [40:51<00:38, 7.68s/it] 99%|█████████▉| 324/328 [40:58<00:30, 7.59s/it] 99%|█████████▉| 325/328 [41:05<00:22, 7.44s/it] 99%|█████████▉| 326/328 [41:12<00:14, 7.37s/it] 100%|█████████▉| 327/328 [41:20<00:07, 7.40s/it] 100%|██████████| 328/328 [41:27<00:00, 7.30s/it][INFO|trainer.py:1823] 2026-07-15 04:50:15,850 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '2487', 'train_samples_per_second': '0.526', 'train_steps_per_second': '0.132', 'train_loss': '2.109', 'epoch': '0.8029'} + 100%|██████████| 328/328 [41:27<00:00, 7.30s/it] 100%|██████████| 328/328 [41:27<00:00, 7.58s/it] +[INFO|trainer.py:3815] 2026-07-15 04:50:15,858 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2 +[INFO|configuration_utils.py:533] 2026-07-15 04:50:15,860 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/config.json +[INFO|configuration_utils.py:816] 2026-07-15 04:50:15,860 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 04:50:16,858 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 04:50:16,880 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/tokenizer_config.json +***** train metrics ***** + epoch = 0.8029 + total_flos = 145083896GF + train_loss = 2.1088 + train_runtime = 0:41:27.28 + train_samples_per_second = 0.526 + train_steps_per_second = 0.132 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v39_v29_curriculum_r0_to_r3_lr6e7_ep08x2/training_loss.png +[WARNING|2026-07-15 04:50:16] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 04:50:16] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 04:50:16,975 >> Dropping the following result as it does not have all the 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