commit cb41ce5fb738eebbc3cdfadf933da7ce3ab19164 Author: ModelHub XC Date: Sun Jul 19 03:31:10 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: lldois/v35_v29_cot_r3_balanced_lr6e7_ep10 Source: Original Platform 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 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100644 index 0000000..36bc982 --- /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: v35_v29_cot_r3_balanced_lr6e7_ep10 + results: [] +--- + + + +# v35_v29_cot_r3_balanced_lr6e7_ep10 + +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_v35_cot_r3_balanced 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: 6e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607185 +- 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: 1.0 + +### 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..fdbb021 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 2.3199594469350605e+17, + "train_loss": 2.076033425085323, + "train_runtime": 3739.4992, + "train_samples_per_second": 0.518, + "train_steps_per_second": 0.13 +} \ 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", + "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 +} diff --git a/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..127248c --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,72 @@ +{ + "created_at": "2026-07-15 00:59:15", + "name": "v35_v29_cot_r3_balanced_lr6e7_ep10", + "base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v35_cot_r3_balanced", + "dataset_manifest": { + "name": "v35_cot_r3_balanced", + "path": "/home/ll/llm4rec/experiments/data/v35_cot_r3_balanced.jsonl", + "records": 80000, + "bytes": 310914202, + "sha256": "4207a66e3c1e6fe035067e219a3a45243c46a9167f1b912eca13d62ec1cdf192", + "requested_components": { + "general": 24000, + "r0": 12000, + "r3_think": 18000, + "r3_direct": 14000, + "old_rec": 5000, + "old_user": 3500, + "old_item": 3500 + }, + "groups": { + "r3": 32000, + "old_user": 3500, + "general": 24000, + "old_rec": 5000, + "r0": 12000, + "old_item": 3500 + }, + "variants": { + "profile_goods_direct": 3190, + "original_competition": 5884, + "official_general_cot": 11903, + "user_array_json_strict": 607, + "official_general_direct": 12097, + "rec_no_think_direct_final": 2500, + "profile_live_direct": 3236, + "caption_to_sid_direct": 5975, + "sid_to_caption_direct": 3085, + "sid_to_caption_cot": 2940, + "profile_video/ad_direct": 3187, + "profile_video/video_direct": 4387, + "profile_live_cot": 4191, + "profile_goods_cot": 4133, + "profile_video/video_cot": 5547, + "profile_video/ad_cot": 4129, + "user_strict_array": 606, + "user_strict_logic": 511, + "item_short_think": 629, + "item_no_think_direct_final": 577, + "user_logic_json_strict": 462, + "user_extra_no_think_logic": 224 + }, + "routes": { + "no_think": 42146, + "think": 37854 + }, + "target_leakage": 0, + "seed": 202607152 + }, + "learning_rate": "6.0e-7", + "epochs": 1.0, + "stages": null, + "note": "Higher R3 CoT ratio with compact Persona-Expansion-Transition traces and direct-route balance.", + "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/v35_v29_cot_r3_balanced_lr6e7_ep10.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_v34_v43.py --single v35_v29_cot_r3_balanced_lr6e7_ep10 --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..b28149d --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:615f1fab6fbcdcd53029b386e3c06accdbe9156f75d9aecd45810deabe05d9c1 +size 1602902832 diff --git a/runs/Jul15_00-59-52_ubuntu-System-Product-Name/events.out.tfevents.1784048392.ubuntu-System-Product-Name.1380389.0 b/runs/Jul15_00-59-52_ubuntu-System-Product-Name/events.out.tfevents.1784048392.ubuntu-System-Product-Name.1380389.0 new file mode 100644 index 0000000..cea1066 --- /dev/null +++ b/runs/Jul15_00-59-52_ubuntu-System-Product-Name/events.out.tfevents.1784048392.ubuntu-System-Product-Name.1380389.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:029901109eb7a33373ab77dc3d28c6b733fb9c5c72506bd33ea54be4b878cda5 +size 16386 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..047d150 --- /dev/null +++ b/train.log @@ -0,0 +1,2308 @@ +[2026-07-15 00:59:15] stage 1/1: /home/ll/llm4rec/experiments/configs/v35_v29_cot_r3_balanced_lr6e7_ep10.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 00:59:19] 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 00:59:19,137 >> 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 00:59:19,140 >> 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 00:59:19,896 >> 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 00:59:19,897 >> 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 00:59:19,897 >> 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 00:59:19,897 >> 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 00:59:20] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v35_cot_r3_balanced.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: 21482 examples [00:00, 195672.79 examples/s] Generating train split: 45959 examples [00:00, 216133.64 examples/s] Generating train split: 75501 examples [00:00, 242238.14 examples/s] Generating train split: 80000 examples [00:00, 236739.79 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/80000 [00:00user +【用户交互历史】： +【2024-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-23】 + --:-- 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<|video_begin|> + 23:48 [视频-长播] <|video_begin|> + 23:58 [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-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|> + 10:04 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:22 [视频-点赞/长播] <|video_begin|> + 10:31 [视频-点赞/长播] <|video_begin|> + 10:54 [视频-长播] <|video_begin|> + 10:54 [视频-长播] <|video_begin|> + 10:54 [视频-点赞/长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:00 [视频-点赞/长播] <|video_begin|> + 11:00 [视频-点赞/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|> + 04:43 [视频-点赞/长播] <|video_begin|> + 05:04 [视频-长播] <|video_begin|> + 05:04 [视频-点赞/长播] <|video_begin|> + 05:04 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 09:26 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 10:31 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 11:40 [视频-长播] <|video_begin|> + 11:40 [视频-点赞/长播] <|video_begin|> + 11:40 [视频-点赞/长播] <|video_begin|> + 11:46 [视频-点赞/长播] <|video_begin|> + 13:14 [视频-长播] <|video_begin|> + 13:14 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 15:31 [视频-长播] <|video_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 23:55 [视频-点赞/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-购买] <|prod_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 16:55 [视频-长播] <|video_begin|> + 17:22 [视频-长播] <|video_begin|> + 17:24 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:29 [视频-长播] <|video_begin|> + 23:29 [视频-长播] <|video_begin|> +【2026-02-04】 + 09:49 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 16:49 [视频-点赞/长播] <|video_begin|> + 16:49 [视频-点赞/长播] <|video_begin|> + 16:57 [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专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑和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）： 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 +核心判定逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B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，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销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 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +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。 +请针对以下主题提取行为逻辑链： +主题：从泛化健康焦虑到具体化保健品探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化健康焦虑到具体化保健品探索", + "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":"从泛化健康焦虑到具体化保健品探索","events":[{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：出现具体的身体部位（膝盖）疼痛问题，并采取了直接的对症处理行为。"},{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"问题泛化：在同一天，关注点从单一的关节问题扩展到另一个中老年男性常见的健康问题（前列腺），表明用户处于对身体多方面健康状况的焦虑期。"},{"date":"2025-11-05","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与方案收敛：在经历了多个具体部位的“被动治疗”后，用户的健康观念发生转变，开始寻求更主动、更根本的解决方案——通过购买明确标有“增强免疫力”的保健品，从“治已病”转向“防未病”。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2022-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2022-10-04】 + --:-- [商品-购买] <|prod_begin|> +【2022-10-16】 + --:-- [商品-购买] <|prod_begin|> +【2022-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-03-28】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-09】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2023-06-26】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-08】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-03】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-26】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-12-18】 + --:-- [商品-购买] <|prod_begin|> +【2024-01-09】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-25】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-26】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-23】 + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-05】 + --:-- [直播-关注] <|living_begin|> +【2025-03-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-17】 + --:-- [直播-关注] <|living_begin|> +【2025-04-18】 + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-05-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-05-31】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-06-17】 + --:-- [直播-关注] <|living_begin|> +【2025-06-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-07-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-19】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-01】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + 01:21 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 23:10 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 20:46 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 12:09 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 10:49 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 01:10 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 17:07 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 01:08 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 11:22 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 12:22 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 00:13 [广告-深度转化] <|ad_begin|> + 00:53 [广告-深度转化] <|ad_begin|> + 04:53 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 01:05 [广告-深度转化] <|ad_begin|> + 01:05 [广告-点击] <|ad_begin|> + 04:58 [广告-深度转化] <|ad_begin|> + 04:58 [广告-点击] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:57 [广告-点击] <|ad_begin|> +【2026-01-14】 + 13:37 [广告-深度转化] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> +【2026-01-15】 + 09:03 [广告-点击] <|ad_begin|> + 12:56 [广告-深度转化] <|ad_begin|> + 12:56 [广告-点击] <|ad_begin|> +【2026-01-16】 + 00:30 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 11:27 [广告-深度转化] <|ad_begin|> + 11:27 [广告-点击] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> +【2026-01-17】 + 10:56 [广告-深度转化] <|ad_begin|> + 10:56 [广告-点击] <|ad_begin|> + 11:16 [广告-深度转化] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 12:04 [广告-深度转化] <|ad_begin|> + 12:04 [广告-点击] <|ad_begin|> +【2026-01-18】 + 21:45 [广告-深度转化] <|ad_begin|> + 21:45 [广告-点击] <|ad_begin|> +【2026-01-19】 + 01:01 [广告-深度转化] <|ad_begin|> + 01:01 [广告-点击] <|ad_begin|> + 05:14 [广告-深度转化] <|ad_begin|> + 05:14 [广告-点击] <|ad_begin|> + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 12:08 [广告-深度转化] <|ad_begin|> + 12:08 [广告-点击] <|ad_begin|> +【2026-01-20】 + 13:05 [广告-点击] <|ad_begin|> +【2026-01-21】 + 00:55 [广告-深度转化] <|ad_begin|> + 00:55 [广告-点击] <|ad_begin|> + 04:09 [广告-深度转化] <|ad_begin|> + 04:09 [广告-点击] <|ad_begin|> + 04:22 [广告-深度转化] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + 07:23 [广告-深度转化] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 10:38 [广告-深度转化] <|ad_begin|> + 10:38 [广告-点击] <|ad_begin|> +【2026-01-24】 + 01:16 [广告-深度转化] <|ad_begin|> + 01:16 [广告-点击] <|ad_begin|> + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 07:09 [广告-深度转化] <|ad_begin|> + 07:09 [广告-点击] <|ad_begin|> + 10:46 [广告-点击] <|ad_begin|> + 12:39 [广告-深度转化] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> +【2026-01-25】 + 00:46 [视频-点赞/长播] <|video_begin|> + 02:46 [视频-点赞/长播] <|video_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 05:20 [视频-点赞/长播] <|video_begin|> + 06:46 [广告-点击] <|ad_begin|> + 06:46 [视频-点赞/长播] <|video_begin|> + 21:19 [广告-深度转化] <|ad_begin|> + 21:19 [广告-点击] <|ad_begin|> +【2026-01-26】 + 05:35 [广告-深度转化] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 05:39 [广告-深度转化] <|ad_begin|> + 05:39 [广告-点击] <|ad_begin|> + 06:42 [视频-长播] <|video_begin|> + 08:30 [广告-点击] <|ad_begin|> + 13:34 [广告-深度转化] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> + 22:05 [广告-深度转化] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> + 22:58 [广告-深度转化] <|ad_begin|> + 22:58 [广告-点击] <|ad_begin|> +【2026-01-27】 + 01:24 [广告-深度转化] <|ad_begin|> + 01:24 [广告-点击] <|ad_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:56 [广告-深度转化] <|ad_begin|> + 04:56 [广告-点击] <|ad_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 06:44 [广告-点击] <|ad_begin|> + 07:41 [视频-长播] <|video_begin|> +【2026-01-28】 + 21:27 [广告-深度转化] <|ad_begin|> + 21:27 [广告-点击] <|ad_begin|> + 21:49 [广告-深度转化] <|ad_begin|> + 21:49 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:31 [视频-长播] <|video_begin|> + 00:52 [广告-深度转化] <|ad_begin|> + 00:52 [广告-点击] <|ad_begin|> + 05:55 [广告-深度转化] <|ad_begin|> + 05:55 [广告-点击] <|ad_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 10:55 [广告-深度转化] <|ad_begin|> + 10:55 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:41 [广告-深度转化] <|ad_begin|> + 00:41 [广告-点击] <|ad_begin|> + 04:14 [广告-深度转化] <|ad_begin|> + 04:14 [广告-点击] <|ad_begin|> + 05:10 [视频-点赞/长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> +【2026-01-31】 + 01:07 [广告-深度转化] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 05:03 [广告-深度转化] <|ad_begin|> + 05:03 [广告-点击] <|ad_begin|> + 06:46 [广告-深度转化] <|ad_begin|> + 06:46 [广告-点击] <|ad_begin|> + 11:06 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> +【2026-02-01】 + 05:30 [视频-长播] <|video_begin|> + 05:30 [视频-长播] <|video_begin|> + 05:48 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 06:36 [广告-深度转化] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> + 12:36 [广告-深度转化] <|ad_begin|> + 12:36 [广告-点击] <|ad_begin|> +【2026-02-02】 + 01:45 [广告-深度转化] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:14 [视频-长播] <|video_begin|> + 04:14 [视频-长播] <|video_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 04:59 [广告-点击] <|ad_begin|> + 06:46 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 10:35 [视频-长播] <|video_begin|> + 11:37 [广告-深度转化] <|ad_begin|> + 11:37 [广告-点击] <|ad_begin|> + 11:50 [广告-点击] <|ad_begin|> + 12:19 [广告-点击] <|ad_begin|> + 21:23 [视频-长播] <|video_begin|> +【2026-02-03】 + 03:07 [广告-深度转化] <|ad_begin|> + 03:07 [广告-点击] <|ad_begin|> + 03:52 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:39 [视频-长播] <|video_begin|> + 09:49 [广告-深度转化] <|ad_begin|> + 09:49 [广告-点击] <|ad_begin|> +【2026-02-04】 + 04:45 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 06:09 [视频-长播] <|video_begin|> + 11:56 [广告-点击] <|ad_begin|> + 12:17 [视频-长播] <|video_begin|> + 13:07 [视频-长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 21:28 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:23 [视频-长播] <|video_begin|> + 00:23 [视频-长播] <|video_begin|> + 00:55 [广告-深度转化] <|ad_begin|> + 00:55 [广告-点击] <|ad_begin|> + 01:03 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 05:02 [广告-深度转化] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + 05:27 [视频-长播] <|video_begin|> + 07:37 [视频-长播] <|video_begin|> + 08:39 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 11:07 [广告-深度转化] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> + 11:15 [视频-长播] <|video_begin|> + 11:15 [视频-长播] <|video_begin|> + 11:27 [广告-点击] <|ad_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）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（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 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +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。 +请面向以下主题提取行为逻辑链： +主题：从皮肤问题到牙齿健康的健康焦点演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从皮肤问题到牙齿健康的健康焦点演化", + "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。/think<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从皮肤问题到牙齿健康的健康焦点演化","events":[{"date":"2025-03-27","action":"[商品-购买] <|prod_begin|>","logic":"问题触发：首次针对特定皮肤问题（“百癣”）购买内服药物，表明用户正面临较为明确的皮肤困扰。"},{"date":"2025-03-31","action":"[商品-购买] <|prod_begin|>","logic":"方案修正与问题细化：在购买内服药仅4天后，用户转而购买针对“皮炎湿疹”的外用“医用液体敷料”。这表明前一个方案可能效果不佳或认知发生变化，问题从泛化的“癣”聚焦到更具体的“皮炎湿疹”，且解决方案从内服转向外用，是典型的基于负反馈的路径修正。"},{"date":"2025-07-01","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与新焦点：在前期购买了泛泛的“固齿”草本片剂后，用户开始点击并关注具有“医用”和“脱敏”特定功能的牙膏。这表明用户对牙齿问题的认知已从笼统的“固齿”保健，升级到需要解决“牙齿脱敏”这一具体痛点，体现了从发散保健到问题聚焦的认知收敛。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-12-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-09】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-13】 + --:-- [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|> +【2025-12-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> + 07:56 [广告-深度转化] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 10:26 [广告-深度转化] <|ad_begin|> + 14:47 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 00:41 [广告-深度转化] <|ad_begin|> + 04:57 [广告-深度转化] <|ad_begin|> + 14:02 [广告-深度转化] <|ad_begin|> + 16:28 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 05:15 [广告-深度转化] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 04:01 [广告-深度转化] <|ad_begin|> + 13:09 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + --:-- [商品-点击] <|prod_begin|> + 03:01 [广告-深度转化] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 09:17 [广告-深度转化] <|ad_begin|> + 09:41 [广告-深度转化] <|ad_begin|> + 12:51 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 00:31 [广告-深度转化] <|ad_begin|> + 02:31 [广告-深度转化] <|ad_begin|> + 08:37 [广告-深度转化] <|ad_begin|> + 10:55 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 02:52 [广告-深度转化] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 06:58 [广告-深度转化] <|ad_begin|> + 14:36 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 00:29 [广告-深度转化] <|ad_begin|> + 01:44 [广告-深度转化] <|ad_begin|> + 04:41 [广告-深度转化] <|ad_begin|> + 09:22 [广告-深度转化] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + 03:48 [广告-深度转化] <|ad_begin|> + 04:40 [广告-深度转化] <|ad_begin|> + 08:42 [广告-深度转化] <|ad_begin|> + 13:58 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 01:59 [广告-深度转化] <|ad_begin|> + 13:59 [广告-深度转化] <|ad_begin|> + 13:59 [广告-点击] <|ad_begin|> + 23:38 [广告-深度转化] <|ad_begin|> + 23:38 [广告-点击] <|ad_begin|> +【2026-01-21】 + 05:17 [广告-深度转化] <|ad_begin|> + 05:17 [广告-点击] <|ad_begin|> + 05:58 [广告-深度转化] <|ad_begin|> + 05:58 [广告-点击] <|ad_begin|> + 06:32 [广告-深度转化] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-23】 + 08:13 [广告-深度转化] <|ad_begin|> + 08:13 [广告-点击] <|ad_begin|> +【2026-01-24】 + 00:35 [广告-点击] <|ad_begin|> + 00:47 [广告-深度转化] <|ad_begin|> + 00:47 [广告-点击] <|ad_begin|> + 08:51 [广告-点击] <|ad_begin|> + 23:24 [广告-深度转化] <|ad_begin|> + 23:24 [广告-点击] <|ad_begin|> +【2026-01-25】 + 04:13 [广告-点击] <|ad_begin|> + 08:06 [广告-深度转化] <|ad_begin|> + 08:06 [广告-点击] <|ad_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + 14:02 [广告-深度转化] <|ad_begin|> + 14:02 [广告-点击] <|ad_begin|> + 23:40 [广告-深度转化] <|ad_begin|> + 23:40 [广告-点击] <|ad_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|> + 00:46 [广告-点击] <|ad_begin|> + 07:29 [广告-深度转化] <|ad_begin|> + 07:29 [广告-点击] <|ad_begin|> + 09:24 [广告-深度转化] <|ad_begin|> + 09:24 [广告-点击] <|ad_begin|> + 13:15 [广告-深度转化] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> +【2026-01-27】 + 08:37 [视频-收藏/长播] <|video_begin|> + 09:04 [广告-点击] <|ad_begin|> + 09:17 [广告-深度转化] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 14:34 [广告-深度转化] <|ad_begin|> + 14:34 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + 05:52 [广告-点击] <|ad_begin|> + 09:24 [广告-点击] <|ad_begin|> + 13:55 [视频-收藏/长播] <|video_begin|> + 14:10 [广告-点击] <|ad_begin|> + 23:32 [广告-深度转化] <|ad_begin|> + 23:32 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:15 [广告-深度转化] <|ad_begin|> + 00:15 [广告-点击] <|ad_begin|> + 06:11 [广告-点击] <|ad_begin|> + 08:32 [视频-收藏/长播] <|video_begin|> + 08:48 [广告-点击] <|ad_begin|> + 08:49 [广告-点击] <|ad_begin|> + 13:18 [广告-深度转化] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:30 [广告-深度转化] <|ad_begin|> + 00:30 [广告-点击] <|ad_begin|> + 04:30 [广告-深度转化] <|ad_begin|> + 04:30 [广告-点击] <|ad_begin|> + 06:47 [广告-深度转化] <|ad_begin|> + 06:47 [广告-点击] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 08:47 [广告-点击] <|ad_begin|> + 09:14 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 10:25 [广告-深度转化] <|ad_begin|> + 10:25 [广告-点击] <|ad_begin|> + 13:00 [广告-深度转化] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> +【2026-01-31】 + 02:21 [广告-点击] <|ad_begin|> + 08:55 [广告-深度转化] <|ad_begin|> + 08:55 [广告-点击] <|ad_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:19 [广告-点击] <|ad_begin|> + 14:31 [视频-长播] <|video_begin|> + 14:32 [视频-长播] <|video_begin|> + 14:51 [广告-深度转化] <|ad_begin|> + 14:51 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 00:50 [广告-深度转化] <|ad_begin|> + 00:50 [广告-点击] <|ad_begin|> + 01:46 [视频-长播] <|video_begin|> + 01:46 [视频-长播] <|video_begin|> + 01:51 [视频-长播] <|video_begin|> + 03:59 [广告-深度转化] <|ad_begin|> + 03:59 [广告-点击] <|ad_begin|> + 04:22 [视频-长播] <|video_begin|> + 04:22 [视频-长播] <|video_begin|> + 04:32 [视频-长播] <|video_begin|> + 04:32 [视频-长播] <|video_begin|> + 05:09 [视频-长播] <|video_begin|> + 05:09 [广告-点击] <|ad_begin|> + 07:05 [视频-长播] <|video_begin|> + 07:05 [视频-长播] <|video_begin|> + 09:18 [广告-深度转化] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:18 [广告-深度转化] <|ad_begin|> + 15:18 [广告-点击] <|ad_begin|> +【2026-02-02】 + 04:07 [视频-点赞/长播] <|video_begin|> + 04:18 [广告-点击] <|ad_begin|> + 08:21 [广告-点击] <|ad_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:28 [广告-点击] <|ad_begin|> + 13:37 [视频-长播] <|video_begin|> + 13:38 [广告-点击] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> +【2026-02-03】 + 03:03 [广告-点击] <|ad_begin|> + 03:04 [视频-长播] <|video_begin|> + 03:04 [视频-长播] <|video_begin|> + 03:06 [视频-长播] <|video_begin|> + 03:10 [广告-点击] <|ad_begin|> + 03:11 [视频-长播] <|video_begin|> + 03:11 [视频-长播] <|video_begin|> + 03:11 [视频-长播] <|video_begin|> + 03:21 [视频-长播] <|video_begin|> + 03:24 [视频-长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:44 [广告-点击] <|ad_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:29 [广告-点击] <|ad_begin|> +【2026-02-04】 + 00:09 [视频-长播] <|video_begin|> + 00:09 [视频-长播] <|video_begin|> + 00:19 [广告-点击] <|ad_begin|> + 04:59 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 05:00 [视频-点赞/长播] <|video_begin|> + 06:00 [广告-点击] <|ad_begin|> + 06:57 [视频-长播] <|video_begin|> + 06:57 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:53 [广告-点击] <|ad_begin|> + 09:30 [广告-点击] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:07 [视频-长播] <|video_begin|> + 00:28 [广告-点击] <|ad_begin|> + 02:49 [视频-长播] <|video_begin|> + 02:59 [视频-长播] <|video_begin|> + 03:31 [广告-深度转化] <|ad_begin|> + 03:31 [广告-点击] <|ad_begin|> + 08:20 [视频-长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:19 [广告-点击] <|ad_begin|> + 09:47 [广告-点击] <|ad_begin|> + 10:00 [广告-深度转化] <|ad_begin|> + 10:00 [广告-点击] <|ad_begin|> +角色任务：你是一个极端严苛的用户行为数据挖掘专家以及数据格式化专家，请根据以上用户交互历史，针对给定的主题，提取出具有行为递进逻辑和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）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、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）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B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，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销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 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +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。 +请根据以下主题提取行为逻辑链： +主题：茶叶消费升级：从性价比到品质导向的决策演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "茶叶消费升级：从性价比到品质导向的决策演化", + "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":"茶叶消费升级：从性价比到品质导向的决策演化","events":[{"date":"2024-12-24","action":"[商品-购买] <|prod_begin|>","logic":"初始状态：首次购买茶叶，明确选择了“性价比款”，表明此刻的核心决策因子是价格和基础饮用功能。"},{"date":"2025-12-10","action":"[商品-点击] <|prod_begin|>","logic":"认知分化与对比探索：在长达近一年的饮用体验后，用户开始主动探索不同品类（从“金骏眉”到“滇红”）、不同产地（“武夷山” vs “云南”）、不同树种（“小种” vs “大叶种”）的茶叶，表明其不再满足于单一品类，开始建立品质对比的认知框架。"},{"date":"2025-12-13","action":"[商品-点击] <|prod_begin|>","logic":"需求场景化：用户的关注点中出现了新的变量“礼盒装”、“送礼茶”，表明其茶叶消费需求从“自饮”拓展到了“社交送礼”，这是对茶叶商品价值认知的一次重要升级。"},{"date":"2025-12-14","action":"[商品-购买] <|prod_begin|>","logic":"决策收敛与品质跃迁：经过密集的点击对比后，用户最终完成了一次购买决策。此次购买的商品包含了“特级”、“明前小芽”、“花果蜜香”等大量在前次购买中未出现的、代表高品质的参数。这标志着用户完成了从追求“性价比”到为“高品质”和“特定风味”付费的认知跃迁，形成了决策闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-11-28】 + --:-- [商品-点击] <|prod_begin|> +【2024-08-18】 + --:-- [商品-点击] <|prod_begin|> +【2024-08-26】 + --:-- [商品-点击] <|prod_begin|> +【2024-09-15】 + --:-- [商品-点击] <|prod_begin|> +【2024-09-22】 + --:-- [商品-点击] <|prod_begin|> +【2024-09-27】 + --:-- [商品-点击] <|prod_begin|> +【2024-10-10】 + --:-- [商品-点击] <|prod_begin|> +【2024-11-02】 + --:-- [商品-点击] <|prod_begin|> +【2024-11-07】 + --:-- [商品-点击] <|prod_begin|> +【2024-11-08】 + --:-- [商品-点击] <|prod_begin|> +【2024-11-11】 + --:-- [商品-点击] <|prod_begin|> +【2024-11-12】 + --:-- [商品-点击] <|prod_begin|> +【2024-12-01】 + --:-- [商品-点击] <|prod_begin|> +【2024-12-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-01-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-03-04】 + --:-- [商品-点击] <|prod_begin|> +【2025-03-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-04-08】 + --:-- [商品-点击] <|prod_begin|> +【2025-04-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-05-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-17】 + --:-- [直播-关注] <|living_begin|> +【2025-07-20】 + --:-- [直播-关注] <|living_begin|> +【2025-07-22】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-26】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-29】 + --:-- [直播-关注] <|living_begin|> +【2025-08-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-25】 + --:-- [直播-关注] <|living_begin|> +【2025-09-06】 + --:-- [直播-关注] <|living_begin|> +【2025-09-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-27】 + --:-- [直播-关注] <|living_begin|> +【2025-09-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-10-05】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-03】 + --:-- [直播-关注] <|living_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-18】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + 04:29 [广告-深度转化] <|ad_begin|> + 04:29 [广告-点击] <|ad_begin|> + 05:23 [广告-点击] <|ad_begin|> +【2025-12-11】 + 01:05 [广告-深度转化] <|ad_begin|> + 01:05 [广告-点击] <|ad_begin|> +【2025-12-12】 + 02:53 [广告-点击] <|ad_begin|> +【2025-12-13】 + 01:46 [广告-深度转化] <|ad_begin|> + 01:46 [广告-点击] <|ad_begin|> + 05:25 [广告-深度转化] <|ad_begin|> + 05:25 [广告-点击] <|ad_begin|> + 05:25 [广告-深度转化] <|ad_begin|> + 05:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-14】 + 02:59 [广告-点击] <|ad_begin|> + 03:40 [广告-深度转化] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 03:40 [广告-深度转化] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 04:55 [广告-深度转化] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 04:55 [广告-深度转化] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 04:58 [广告-点击] <|ad_begin|> + 05:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + 03:25 [广告-深度转化] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + 04:03 [广告-深度转化] <|ad_begin|> + 04:03 [广告-点击] <|ad_begin|> + 07:05 [广告-深度转化] <|ad_begin|> + 07:05 [广告-点击] <|ad_begin|> +【2025-12-23】 + 09:02 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + 12:12 [广告-点击] <|ad_begin|> +【2025-12-26】 + 05:51 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [直播-关注] <|living_begin|> +【2026-01-01】 + 09:56 [广告-点击] <|ad_begin|> +【2026-01-13】 + 12:46 [广告-点击] <|ad_begin|> +【2026-01-16】 + 07:12 [广告-点击] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> +【2026-01-17】 + 10:50 [广告-点击] <|ad_begin|> +【2026-01-20】 + 01:43 [广告-点击] <|ad_begin|> + 02:11 [广告-点击] <|ad_begin|> + 02:19 [广告-点击] <|ad_begin|> +【2026-01-22】 + 11:46 [视频-关注/长播] <|video_begin|> + 12:20 [视频-点赞/长播] <|video_begin|> + 13:12 [视频-点赞/长播] <|video_begin|> + 13:12 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + 03:32 [视频-浏览] <|video_begin|> + 03:32 [视频-浏览] <|video_begin|> + 06:25 [视频-点赞/长播] <|video_begin|> + 14:15 [广告-点击] <|ad_begin|> + 23:46 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + 00:37 [视频-关注/长播] <|video_begin|> + 00:38 [视频-浏览] <|video_begin|> + 01:24 [视频-点赞/长播] <|video_begin|> + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 05:19 [视频-点赞/长播] <|video_begin|> +【2026-01-26】 + 06:25 [视频-点赞/长播] <|video_begin|> + 06:29 [视频-点赞/长播] <|video_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + 12:42 [视频-收藏/长播] <|video_begin|> + 13:03 [视频-关注/长播] <|video_begin|> +【2026-01-27】 + 03:16 [广告-深度转化] <|ad_begin|> + 03:16 [广告-点击] <|ad_begin|> + 03:18 [视频-点赞/长播] <|video_begin|> + 04:00 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 02:16 [广告-点击] <|ad_begin|> + 02:57 [广告-点击] <|ad_begin|> + 04:49 [视频-点赞/长播] <|video_begin|> + 05:24 [视频-浏览] <|video_begin|> + 13:33 [视频-收藏/长播] <|video_begin|> +【2026-01-29】 + 00:39 [视频-点赞/长播] <|video_begin|> + 01:22 [视频-转发/长播] <|video_begin|> + 01:25 [视频-点赞/长播] <|video_begin|> + 04:55 [视频-浏览] <|video_begin|> + 06:51 [视频-关注/长播] <|video_begin|> + 07:55 [视频-关注/长播] <|video_begin|> +【2026-01-30】 + 03:44 [视频-浏览] <|video_begin|> + 06:09 [视频-点赞/长播] <|video_begin|> + 06:10 [视频-转发/长播] <|video_begin|> + 12:44 [视频-点赞/长播] <|video_begin|> +【2026-01-31】 + 00:17 [广告-深度转化] <|ad_begin|> + 00:17 [广告-点击] <|ad_begin|> + 04:37 [视频-点赞/长播] <|video_begin|> + 12:14 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 01:52 [视频-浏览] <|video_begin|> + 02:33 [视频-关注/长播] <|video_begin|> + 09:01 [视频-转发/长播] <|video_begin|> + 09:43 [视频-收藏/长播] <|video_begin|> +【2026-02-02】 + 10:39 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 01:22 [广告-深度转化] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + 01:23 [广告-深度转化] <|ad_begin|> + 01:23 [广告-点击] <|ad_begin|> + 01:24 [广告-深度转化] <|ad_begin|> + 01:24 [广告-点击] <|ad_begin|> + 01:32 [广告-深度转化] <|ad_begin|> + 01:32 [广告-点击] <|ad_begin|> + 01:32 [广告-深度转化] <|ad_begin|> + 01:32 [广告-点击] <|ad_begin|> + 01:33 [广告-深度转化] <|ad_begin|> + 01:33 [广告-点击] <|ad_begin|> + 01:36 [广告-深度转化] <|ad_begin|> + 01:36 [广告-点击] <|ad_begin|> + 03:33 [广告-点击] <|ad_begin|> + 04:08 [广告-点击] <|ad_begin|> + 06:29 [视频-浏览] <|video_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 08:03 [视频-点赞/长播] <|video_begin|> + 08:18 [视频-点赞/长播] <|video_begin|> + 08:37 [广告-深度转化] <|ad_begin|> + 08:37 [广告-点击] <|ad_begin|> + 08:37 [广告-深度转化] <|ad_begin|> + 08:37 [广告-点击] <|ad_begin|> + 15:35 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + 06:25 [广告-深度转化] <|ad_begin|> + 06:25 [广告-点击] <|ad_begin|> + 06:28 [广告-深度转化] <|ad_begin|> + 06:28 [广告-点击] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:30 [广告-点击] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:30 [广告-点击] <|ad_begin|> + 10:23 [视频-浏览] <|video_begin|> + 10:50 [视频-浏览] <|video_begin|> + 11:51 [视频-点赞/长播] <|video_begin|> + 11:54 [广告-深度转化] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> +【2026-02-05】 + 04:05 [视频-浏览] <|video_begin|> + 05:00 [视频-转发/长播] <|video_begin|> + 06:00 [视频-点赞/长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:12 [视频-长播] <|video_begin|> + 09:12 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 10:49 [视频-长播] <|video_begin|> + 10:49 [视频-长播] <|video_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|> + 10:53 [视频-长播] <|video_begin|> + 10:53 [视频-长播] <|video_begin|> + 10:57 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 11:02 [视频-长播] <|video_begin|> + 11:06 [视频-长播] <|video_begin|> + 11:06 [视频-长播] <|video_begin|> + 11:06 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:19 [广告-点击] <|ad_begin|> + 11:25 [视频-长播] <|video_begin|> + 11:25 [视频-长播] <|video_begin|> + 11:28 [视频-长播] <|video_begin|> + 11:29 [视频-长播] <|video_begin|> + 11:29 [视频-长播] <|video_begin|> + 11:32 [视频-长播] <|video_begin|> + 11:38 [视频-长播] <|video_begin|> + 11:39 [视频-长播] <|video_begin|> + 11:42 [视频-长播] <|video_begin|> + 11:42 [视频-长播] <|video_begin|> + 11:42 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 13:03 [视频-长播] <|video_begin|> + 13:12 [视频-长播] <|video_begin|> + 13:13 [广告-深度转化] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:14 [广告-点击] <|ad_begin|> + 13:15 [广告-深度转化] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 13:16 [广告-点击] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> + 13:18 [广告-深度转化] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> + 13:20 [广告-深度转化] <|ad_begin|> + 13:20 [广告-点击] <|ad_begin|> + 13:20 [广告-深度转化] <|ad_begin|> + 13:20 [广告-点击] <|ad_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 13:22 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> + 13:26 [视频-长播] <|video_begin|> + 13:26 [视频-长播] <|video_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> + 13:27 [视频-长播] <|video_begin|> + 13:27 [视频-长播] <|video_begin|> + 13:27 [视频-长播] <|video_begin|> + 13:33 [视频-长播] <|video_begin|> + 13:33 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:44 [视频-长播] <|video_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑和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）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、视频-浏览、视频-长播、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 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 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +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。 +请根据以下主题提取行为逻辑链： +主题：从泛化兴趣到特定IP的玩具消费收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化兴趣到特定IP的玩具消费收敛", + "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。/think<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从泛化兴趣到特定IP的玩具消费收敛","events":[{"date":"2024-08-18","action":"[商品-点击] <|prod_begin|>","logic":"兴趣触发：开始关注儿童大型室内玩具，兴趣点较为宽泛，集中在“亲子”、“家用”等属性。"},{"date":"2025-09-21","action":"[商品-点击] <|prod_begin|>","logic":"兴趣探索：在浏览了多种玩具后，兴趣转向功能更复杂、带有“遥控”、“可坐人”属性的工程车玩具，但仍属于泛化的玩具品类探索。"},{"date":"2025-12-09","action":"[商品-点击] <|prod_begin|>","logic":"兴趣聚焦：首次出现强IP属性（奥特曼）的商品，兴趣从玩具的功能/类型转向了具体文化符号，但关注点是服装，而非核心玩具。"},{"date":"2025-12-14","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与深化：兴趣完全收敛于“奥特曼”IP，并且从普通童装升级为包含“皮套”、“cos”、“头盔”等更具角色扮演性质的特定商品，体现了对该IP兴趣的加深和消费意图的明确化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-13】 + 09:13 [广告-深度转化] <|ad_begin|> + 09:13 [广告-点击] <|ad_begin|> +【2025-12-26】 + 12:57 [广告-点击] <|ad_begin|> +【2025-12-31】 + 14:40 [广告-点击] <|ad_begin|> +【2026-01-10】 + 03:32 [广告-点击] <|ad_begin|> +【2026-01-14】 + 10:58 [广告-点击] <|ad_begin|> +【2026-01-16】 + 12:10 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> +【2026-01-20】 + 02:15 [广告-点击] <|ad_begin|> +【2026-01-23】 + 00:24 [广告-点击] <|ad_begin|> + 02:29 [视频-转发/点赞/长播] <|video_begin|> + 11:43 [视频-转发/长播] <|video_begin|> +【2026-01-24】 + 06:00 [视频-转发/点赞/长播] <|video_begin|> + 06:52 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 12:21 [视频-浏览] <|video_begin|> + 14:11 [视频-转发/点赞/长播] <|video_begin|> + 23:05 [广告-点击] <|ad_begin|> +【2026-01-26】 + 06:38 [视频-收藏/长播] <|video_begin|> + 07:26 [广告-点击] <|ad_begin|> + 15:40 [视频-关注/长播] <|video_begin|> + 16:47 [广告-深度转化] <|ad_begin|> + 16:47 [广告-点击] <|ad_begin|> + 17:18 [广告-点击] <|ad_begin|> +【2026-01-27】 + 06:33 [视频-收藏/点赞/长播] <|video_begin|> + 06:50 [视频-点赞] <|video_begin|> +【2026-01-28】 + 06:46 [视频-评论/长播] <|video_begin|> + 07:12 [视频-关注/收藏/长播] <|video_begin|> + 07:15 [视频-评论/长播] <|video_begin|> + 08:24 [视频-转发/长播] <|video_begin|> + 15:28 [视频-浏览] <|video_begin|> +【2026-01-29】 + 02:26 [广告-点击] <|ad_begin|> + 07:00 [广告-点击] <|ad_begin|> +【2026-01-30】 + 03:06 [广告-点击] <|ad_begin|> + 05:26 [视频-评论/长播] <|video_begin|> + 06:11 [视频-评论/点赞/长播] <|video_begin|> + 08:58 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-31】 + 16:23 [视频-评论/点赞/长播] <|video_begin|> + 17:12 [视频-评论/点赞/长播] <|video_begin|> + 17:45 [视频-收藏/长播] <|video_begin|> + 17:58 [视频-评论/点赞/长播] <|video_begin|> +【2026-02-01】 + 03:54 [视频-评论/点赞/长播] <|video_begin|> + 04:29 [视频-评论/点赞/长播] <|video_begin|> + 06:53 [视频-收藏/长播] <|video_begin|> +【2026-02-02】 + 02:17 [视频-评论/长播] <|video_begin|> + 04:03 [视频-评论/点赞/长播] <|video_begin|> + 05:05 [视频-评论/点赞/长播] <|video_begin|> + 09:18 [广告-深度转化] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 10:09 [广告-深度转化] <|ad_begin|> + 10:09 [广告-点击] <|ad_begin|> + 11:09 [视频-评论/点赞/长播] <|video_begin|> + 13:41 [广告-点击] <|ad_begin|> + 15:03 [视频-关注/长播] <|video_begin|> +【2026-02-03】 + 02:29 [广告-深度转化] <|ad_begin|> + 02:29 [广告-点击] <|ad_begin|> + 02:39 [视频-收藏/长播] <|video_begin|> + 03:41 [视频-评论/点赞/长播] <|video_begin|> + 13:50 [视频-转发/长播] <|video_begin|> + 15:38 [视频-收藏/长播] <|video_begin|> + 16:27 [视频-收藏/长播] <|video_begin|> + 16:38 [视频-收藏/长播] <|video_begin|> + 16:40 [视频-收藏/长播] <|video_begin|> + 18:24 [视频-评论/收藏/点赞/长播] <|video_begin|> + 19:02 [视频-收藏/点赞/长播] <|video_begin|> + 21:20 [视频-点赞/长播] <|video_begin|> + 21:37 [视频-点赞/长播] <|video_begin|> + 21:37 [视频-收藏/长播] <|video_begin|> + 21:44 [视频-点赞/长播] <|video_begin|> + 21:51 [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-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|> +【2026-02-04】 + 03:59 [视频-点赞/长播] <|video_begin|> + 03:59 [视频-点赞/长播] <|video_begin|> + 04:04 [视频-点赞/长播] <|video_begin|> + 04:16 [视频-点赞/长播] <|video_begin|> + 04:16 [视频-点赞/长播] <|video_begin|> + 04:16 [视频-点赞/长播] <|video_begin|> + 04:20 [视频-点赞/长播] <|video_begin|> + 04:35 [视频-点赞/长播] <|video_begin|> + 05:57 [视频-点赞/长播] <|video_begin|> + 05:59 [视频-点赞/长播] <|video_begin|> + 05:59 [视频-点赞/长播] <|video_begin|> + 06:06 [视频-点赞/长播] <|video_begin|> + 06:06 [视频-点赞/长播] <|video_begin|> + 06:09 [视频-点赞/长播] <|video_begin|> + 10:11 [广告-点击] <|ad_begin|> + 13:20 [广告-点击] <|ad_begin|> + 15:59 [视频-点赞/长播] <|video_begin|> + 16:05 [视频-点赞/长播] <|video_begin|> + 16:30 [视频-点赞/长播] <|video_begin|> + 16:30 [视频-点赞/长播] <|video_begin|> + 16:30 [视频-点赞/长播] <|video_begin|> + 16:32 [视频-点赞/长播] <|video_begin|> + 17:15 [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|> +【2026-02-05】 + 02:24 [视频-长播] <|video_begin|> + 02:24 [视频-点赞/长播] <|video_begin|> + 02:24 [视频-点赞/长播] <|video_begin|> + 02:35 [视频-长播] <|video_begin|> + 02:35 [视频-点赞/长播] <|video_begin|> + 02:35 [视频-点赞/长播] <|video_begin|> + 02:48 [视频-长播] <|video_begin|> + 02:48 [视频-长播] <|video_begin|> + 02:48 [视频-点赞/长播] <|video_begin|> + 02:53 [视频-长播] <|video_begin|> + 03:05 [视频-长播] <|video_begin|> + 03:05 [视频-长播] <|video_begin|> + 03:05 [视频-点赞/长播] <|video_begin|> + 03:05 [视频-点赞/长播] <|video_begin|> + 03:29 [视频-长播] <|video_begin|> + 03:29 [视频-点赞/长播] <|video_begin|> + 03:29 [视频-点赞/长播] <|video_begin|> + 07:35 [视频-点赞/长播] <|video_begin|> + 07:35 [视频-点赞/长播] <|video_begin|> + 09:01 [视频-点赞/长播] <|video_begin|> + 09:13 [视频-点赞/长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:19 [视频-长播] <|video_begin|> + 09:22 [视频-长播] <|video_begin|> + 09:22 [视频-点赞/长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:33 [视频-点赞/长播] <|video_begin|> + 09:34 [视频-评论/点赞/长播] <|video_begin|> + 09:36 [视频-长播] <|video_begin|> + 09:47 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<|prod_begin|>","logic":"初始触发：出现具体的身体部位（膝盖）疼痛问题，并采取了直接的对症处理行为。"},{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"问题泛化：在同一天，关注点从单一的关节问题扩展到另一个中老年男性常见的健康问题（前列腺），表明用户处于对身体多方面健康状况的焦虑期。"},{"date":"2025-11-05","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与方案收敛：在经历了多个具体部位的“被动治疗”后，用户的健康观念发生转变，开始寻求更主动、更根本的解决方案——通过购买明确标有“增强免疫力”的保健品，从“治已病”转向“防未病”。"}]}}<|im_end|> + + +{"logic_chain":{"name":"从皮肤问题到牙齿健康的健康焦点演化","events":[{"date":"2025-03-27","action":"[商品-购买] <|prod_begin|>","logic":"问题触发：首次针对特定皮肤问题（“百癣”）购买内服药物，表明用户正面临较为明确的皮肤困扰。"},{"date":"2025-03-31","action":"[商品-购买] <|prod_begin|>","logic":"方案修正与问题细化：在购买内服药仅4天后，用户转而购买针对“皮炎湿疹”的外用“医用液体敷料”。这表明前一个方案可能效果不佳或认知发生变化，问题从泛化的“癣”聚焦到更具体的“皮炎湿疹”，且解决方案从内服转向外用，是典型的基于负反馈的路径修正。"},{"date":"2025-07-01","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与新焦点：在前期购买了泛泛的“固齿”草本片剂后，用户开始点击并关注具有“医用”和“脱敏”特定功能的牙膏。这表明用户对牙齿问题的认知已从笼统的“固齿”保健，升级到需要解决“牙齿脱敏”这一具体痛点，体现了从发散保健到问题聚焦的认知收敛。"}]}}<|im_end|> + + +{"logic_chain":{"name":"茶叶消费升级：从性价比到品质导向的决策演化","events":[{"date":"2024-12-24","action":"[商品-购买] <|prod_begin|>","logic":"初始状态：首次购买茶叶，明确选择了“性价比款”，表明此刻的核心决策因子是价格和基础饮用功能。"},{"date":"2025-12-10","action":"[商品-点击] <|prod_begin|>","logic":"认知分化与对比探索：在长达近一年的饮用体验后，用户开始主动探索不同品类（从“金骏眉”到“滇红”）、不同产地（“武夷山” vs “云南”）、不同树种（“小种” vs “大叶种”）的茶叶，表明其不再满足于单一品类，开始建立品质对比的认知框架。"},{"date":"2025-12-13","action":"[商品-点击] <|prod_begin|>","logic":"需求场景化：用户的关注点中出现了新的变量“礼盒装”、“送礼茶”，表明其茶叶消费需求从“自饮”拓展到了“社交送礼”，这是对茶叶商品价值认知的一次重要升级。"},{"date":"2025-12-14","action":"[商品-购买] <|prod_begin|>","logic":"决策收敛与品质跃迁：经过密集的点击对比后，用户最终完成了一次购买决策。此次购买的商品包含了“特级”、“明前小芽”、“花果蜜香”等大量在前次购买中未出现的、代表高品质的参数。这标志着用户完成了从追求“性价比”到为“高品质”和“特定风味”付费的认知跃迁，形成了决策闭环。"}]}}<|im_end|> + + +{"logic_chain":{"name":"从泛化兴趣到特定IP的玩具消费收敛","events":[{"date":"2024-08-18","action":"[商品-点击] <|prod_begin|>","logic":"兴趣触发：开始关注儿童大型室内玩具，兴趣点较为宽泛，集中在“亲子”、“家用”等属性。"},{"date":"2025-09-21","action":"[商品-点击] <|prod_begin|>","logic":"兴趣探索：在浏览了多种玩具后，兴趣转向功能更复杂、带有“遥控”、“可坐人”属性的工程车玩具，但仍属于泛化的玩具品类探索。"},{"date":"2025-12-09","action":"[商品-点击] <|prod_begin|>","logic":"兴趣聚焦：首次出现强IP属性（奥特曼）的商品，兴趣从玩具的功能/类型转向了具体文化符号，但关注点是服装，而非核心玩具。"},{"date":"2025-12-14","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与深化：兴趣完全收敛于“奥特曼”IP，并且从普通童装升级为包含“皮套”、“cos”、“头盔”等更具角色扮演性质的特定商品，体现了对该IP兴趣的加深和消费意图的明确化。"}]}}<|im_end|> + + +["<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 00:59:51,967 >> 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 00:59:51,968 >> 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 00:59:51] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 00:59:52] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 00:59:52,358 >> 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 00:59:52,358 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 00:59:52,526 >> 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 00:59:52,822 >> 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 00:59:52,823 >> 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 00:59:52] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 00:59:52] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 00:59:52] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 00:59:52] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 00:59:52] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 00:59:52,958 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 00:59:52,958 >> Num examples = 1,937 +[INFO|trainer.py:1471] 2026-07-15 00:59:52,958 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-15 00:59:52,958 >> Num update steps per epoch = 485 +[INFO|trainer.py:1473] 2026-07-15 00:59:52,958 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 00:59:52,958 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 00:59:52,958 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 00:59:52,958 >> Total optimization steps = 485 +[INFO|trainer.py:1479] 2026-07-15 00:59:52,958 >> Number of trainable parameters = 801,433,600 + 0%| | 0/485 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 0%| | 1/485 [00:08<1:10:28, 8.74s/it] 0%| | 2/485 [00:16<1:06:27, 8.26s/it] 1%| | 3/485 [00:23<1:02:23, 7.77s/it] 1%| | 4/485 [00:31<1:01:58, 7.73s/it] 1%| | 5/485 [00:38<1:01:02, 7.63s/it] 1%| | 6/485 [00:46<1:01:09, 7.66s/it] 1%|▏ | 7/485 [00:53<1:00:06, 7.55s/it] 2%|▏ | 8/485 [01:02<1:01:10, 7.69s/it] 2%|▏ | 9/485 [01:09<1:00:49, 7.67s/it] 2%|▏ | 10/485 [01:17<1:01:09, 7.73s/it] {'loss': '2.075', 'grad_norm': '4.406', 'learning_rate': '3.6e-07', 'epoch': '0.02065'} + 2%|▏ | 10/485 [01:17<1:01:09, 7.73s/it] 2%|▏ | 11/485 [01:25<1:01:18, 7.76s/it] 2%|▏ | 12/485 [01:33<1:01:50, 7.85s/it] 3%|▎ | 13/485 [01:41<1:01:30, 7.82s/it] 3%|▎ | 14/485 [01:49<1:02:51, 8.01s/it] 3%|▎ | 15/485 [01:56<59:38, 7.61s/it] 3%|▎ | 16/485 [02:03<59:30, 7.61s/it] 4%|▎ | 17/485 [02:11<58:36, 7.51s/it] 4%|▎ | 18/485 [02:19<59:46, 7.68s/it] 4%|▍ | 19/485 [02:27<59:57, 7.72s/it] 4%|▍ | 20/485 [02:34<59:59, 7.74s/it] {'loss': '2.085', 'grad_norm': '16', 'learning_rate': '5.999e-07', 'epoch': '0.0413'} + 4%|▍ | 20/485 [02:34<59:59, 7.74s/it] 4%|▍ | 21/485 [02:42<1:00:10, 7.78s/it] 5%|▍ | 22/485 [02:50<59:42, 7.74s/it] 5%|▍ | 23/485 [02:58<59:47, 7.76s/it] 5%|▍ | 24/485 [03:05<59:13, 7.71s/it] 5%|▌ | 25/485 [03:13<1:00:13, 7.85s/it] 5%|▌ | 26/485 [03:21<1:00:20, 7.89s/it] 6%|▌ | 27/485 [03:28<57:54, 7.59s/it] 6%|▌ | 28/485 [03:37<1:00:46, 7.98s/it] 6%|▌ | 29/485 [03:44<58:08, 7.65s/it] 6%|▌ | 30/485 [03:52<59:22, 7.83s/it] {'loss': '2.207', 'grad_norm': '30', 'learning_rate': '5.987e-07', 'epoch': '0.06195'} + 6%|▌ | 30/485 [03:52<59:22, 7.83s/it] 6%|▋ | 31/485 [03:58<55:23, 7.32s/it] 7%|▋ | 32/485 [04:06<55:32, 7.36s/it] 7%|▋ | 33/485 [04:13<54:59, 7.30s/it] 7%|▋ | 34/485 [04:21<57:17, 7.62s/it] 7%|▋ | 35/485 [04:29<56:16, 7.50s/it] 7%|▋ | 36/485 [04:35<54:22, 7.27s/it] 8%|▊ | 37/485 [04:43<55:10, 7.39s/it] 8%|▊ | 38/485 [04:50<54:43, 7.35s/it] 8%|▊ | 39/485 [04:58<54:39, 7.35s/it] 8%|▊ | 40/485 [05:06<57:07, 7.70s/it] {'loss': '2.106', 'grad_norm': '5.531', 'learning_rate': '5.961e-07', 'epoch': '0.0826'} + 8%|▊ | 40/485 [05:06<57:07, 7.70s/it] 8%|▊ | 41/485 [05:15<58:33, 7.91s/it] 9%|▊ | 42/485 [05:23<58:31, 7.93s/it] 9%|▉ | 43/485 [05:30<57:59, 7.87s/it] 9%|▉ | 44/485 [05:38<58:03, 7.90s/it] 9%|▉ | 45/485 [05:46<57:14, 7.81s/it] 9%|▉ | 46/485 [05:54<57:48, 7.90s/it] 10%|▉ | 47/485 [06:02<57:37, 7.89s/it] 10%|▉ | 48/485 [06:10<57:14, 7.86s/it] 10%|█ | 49/485 [06:17<56:16, 7.74s/it] 10%|█ | 50/485 [06:25<56:09, 7.75s/it] {'loss': '2.149', 'grad_norm': '7.531', 'learning_rate': '5.923e-07', 'epoch': '0.1033'} + 10%|█ | 50/485 [06:25<56:09, 7.75s/it] 11%|█ | 51/485 [06:33<56:24, 7.80s/it] 11%|█ | 52/485 [06:40<54:59, 7.62s/it] 11%|█ | 53/485 [06:49<57:55, 8.05s/it] 11%|█ | 54/485 [06:57<57:20, 7.98s/it] 11%|█▏ | 55/485 [07:05<58:12, 8.12s/it] 12%|█▏ | 56/485 [07:13<57:40, 8.07s/it] 12%|█▏ | 57/485 [07:21<56:40, 7.94s/it] 12%|█▏ | 58/485 [07:28<54:56, 7.72s/it] 12%|█▏ | 59/485 [07:35<52:39, 7.42s/it] 12%|█▏ | 60/485 [07:42<52:24, 7.40s/it] {'loss': '2.233', 'grad_norm': '22.25', 'learning_rate': '5.871e-07', 'epoch': '0.1239'} + 12%|█▏ | 60/485 [07:42<52:24, 7.40s/it] 13%|█▎ | 61/485 [07:50<52:43, 7.46s/it] 13%|█▎ | 62/485 [07:57<53:14, 7.55s/it] 13%|█▎ | 63/485 [08:06<54:43, 7.78s/it] 13%|█▎ | 64/485 [08:14<55:09, 7.86s/it] 13%|█▎ | 65/485 [08:21<52:41, 7.53s/it] 14%|█▎ | 66/485 [08:28<52:11, 7.47s/it] 14%|█▍ | 67/485 [08:36<52:48, 7.58s/it] 14%|█▍ | 68/485 [08:43<52:09, 7.50s/it] 14%|█▍ | 69/485 [08:49<49:27, 7.13s/it] 14%|█▍ | 70/485 [08:57<50:16, 7.27s/it] {'loss': '2.16', 'grad_norm': '25', 'learning_rate': '5.807e-07', 'epoch': '0.1446'} + 14%|█▍ | 70/485 [08:57<50:16, 7.27s/it] 15%|█▍ | 71/485 [09:05<51:06, 7.41s/it] 15%|█▍ | 72/485 [09:12<51:00, 7.41s/it] 15%|█▌ | 73/485 [09:19<50:22, 7.34s/it] 15%|█▌ | 74/485 [09:27<51:14, 7.48s/it] 15%|█▌ | 75/485 [09:36<53:44, 7.86s/it] 16%|█▌ | 76/485 [09:44<54:18, 7.97s/it] 16%|█▌ | 77/485 [09:52<54:00, 7.94s/it] 16%|█▌ | 78/485 [10:00<54:25, 8.02s/it] 16%|█▋ | 79/485 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'0.9706'} + 97%|█████████▋| 470/485 [1:00:29<01:55, 7.70s/it] 97%|█████████▋| 471/485 [1:00:36<01:46, 7.58s/it] 97%|█████████▋| 472/485 [1:00:44<01:39, 7.64s/it] 98%|█████████▊| 473/485 [1:00:52<01:33, 7.79s/it] 98%|█████████▊| 474/485 [1:01:00<01:26, 7.84s/it] 98%|█████████▊| 475/485 [1:01:08<01:19, 7.91s/it] 98%|█████████▊| 476/485 [1:01:17<01:12, 8.10s/it] 98%|█████████▊| 477/485 [1:01:24<01:02, 7.87s/it] 99%|█████████▊| 478/485 [1:01:32<00:53, 7.70s/it] 99%|█████████▉| 479/485 [1:01:40<00:47, 7.87s/it] 99%|█████████▉| 480/485 [1:01:47<00:38, 7.70s/it] {'loss': '2.08', 'grad_norm': '10.62', 'learning_rate': '2.412e-10', 'epoch': '0.9912'} + 99%|█████████▉| 480/485 [1:01:47<00:38, 7.70s/it] 99%|█████████▉| 481/485 [1:01:55<00:31, 7.76s/it] 99%|█████████▉| 482/485 [1:02:01<00:21, 7.20s/it] 100%|█████████▉| 483/485 [1:02:09<00:14, 7.34s/it] 100%|█████████▉| 484/485 [1:02:17<00:07, 7.68s/it] 100%|██████████| 485/485 [1:02:19<00:00, 5.94s/it][INFO|trainer.py:1823] 2026-07-15 02:02:12,423 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '3739', 'train_samples_per_second': '0.518', 'train_steps_per_second': '0.13', 'train_loss': '2.076', 'epoch': '1'} + 100%|██████████| 485/485 [1:02:19<00:00, 5.94s/it] 100%|██████████| 485/485 [1:02:19<00:00, 7.71s/it] +[INFO|trainer.py:3815] 2026-07-15 02:02:12,459 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10 +[INFO|configuration_utils.py:533] 2026-07-15 02:02:12,460 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/config.json +[INFO|configuration_utils.py:816] 2026-07-15 02:02:12,460 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 02:02:13,402 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 02:02:13,423 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 216063060GF + train_loss = 2.076 + train_runtime = 1:02:19.49 + train_samples_per_second = 0.518 + train_steps_per_second = 0.13 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v35_v29_cot_r3_balanced_lr6e7_ep10/training_loss.png +[WARNING|2026-07-15 02:02:13] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 02:02:13] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 02:02:13,548 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 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