commit 01b0d23a076387a4c5f8d351a608cdff201f0879 Author: ModelHub XC Date: Wed Jul 29 08:34:22 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: lldois/v29_v19_user_world_guard_lr8e7_ep018 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 -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..8897611 --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v19_v15_dualmode_fast_lr5e6_ep055 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v29_v19_user_world_guard_lr8e7_ep018 + results: [] +--- + + + +# v29_v19_user_world_guard_lr8e7_ep018 + +This model is a fine-tuned version of [lldois/v19_v15_dualmode_fast_lr5e6_ep055](https://huggingface.co/lldois/v19_v15_dualmode_fast_lr5e6_ep055) on the exp_v19_user_world_guard_no_template 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: 8e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607291 +- 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: 0.02 +- num_epochs: 0.18 + +### 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..6efc793 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.1830574488802337, + "total_flos": 2.2427473783689216e+16, + "train_loss": 1.4800728635585054, + "train_runtime": 394.9336, + "train_samples_per_second": 0.468, + "train_steps_per_second": 0.119 +} \ 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..6fee6cd --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,66 @@ +{ + "created_at": "2026-07-12 04:25:55", + "run": { + "name": "v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v19_user_world_guard_no_template", + "model_path": "/home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055", + "lr": "8.0e-7", + "epochs": 0.18, + "warmup": 0.02, + "scheduler": "cosine", + "seed": 202607291, + "note": "v19 continuation. Very small user/world guard update with strict user JSON, original cleaned CoT, and light item replay. Goal: preserve the best CoT total while nudging user2/world back up without adding template repetition.", + "config": "/home/ll/llm4rec/experiments/configs/v29_v19_user_world_guard_lr8e7_ep018.yaml" + }, + "dataset_manifest": { + "name": "v19_user_world_guard_no_template", + "path": "/home/ll/llm4rec/experiments/data/v19_user_world_guard_no_template.jsonl", + "records": 18091, + "groups": { + "user": 5094, + "item": 7997, + "rec": 5000 + }, + "variants": { + "user_strict_array": 1588, + "item_no_think_direct_final": 2805, + "rec_cot_pattern_clean": 2800, + "raw": 1600, + "rec_no_think_direct_final": 2200, + "user_strict_logic": 1304, + "item_short_think": 2792, + "item_compact_cot": 2400, + "user_extra_no_think_logic": 602 + }, + "sha256": "d540db2637094ad95fb48e3daade2a84576def30b4e06a840f8af83ce46a45bb", + "seed": 202607112 + }, + "dataset_recipe": "v19 continuation: very small LR user/world preservation update with raw user CoT, strict JSON dual-route replay, light cleaned rec CoT, no short-template synthetic reasoning, and item replay.", + "cot_policy": "Preserve /think reasoning supervision and do not use v7_final_only as a CoT training base. For /no_think prompts, train pure final answers without generated tags. This is route-specific behavior, not global CoT removal.", + "raw_counts": { + "rec": 19204, + "item": 10384, + "user": 2892 + }, + "eval_observations": { + "v07": "best local score so far: total=0.8978, eval_time≈47.3min; fast final outputs likely help.", + "v15": "best CoT-preserving score so far: total=0.8778, eval_time≈70.1min; logs show repeated tokens, JSON shell errors, prompt leakage, and verbose /no_think outputs.", + "v19": "best CoT-native continuation so far: total=0.8855, eval_time≈48.1min; user1 and rec4 improved but world dropped.", + "v20": "v7 final-only continuation with CoT restore failed as a CoT route: total=0.8527, item fell to 0.1840; do not use v7 as future CoT base.", + "v22": "scratch official-base 3 epoch clean CoT underperformed: total=0.8217; item/world preserved but user and rec2 are weak.", + "v23": "scratch official-base 5 epoch low-LR guard failed badly: total=0.6990; item/user collapse suggests long scratch SFT is not viable with current data mix.", + "v24": "v15 light repair is best among v22-v24 but still only total=0.8364; user2 improves but rec/world do not recover.", + "v25": "v19 product-heavy repair did not beat v19: total=0.8793. Logs show heavy repeated product tokens and repeated short-think phrases; avoid this over-sampling pattern.", + "v26": "best latest batch and fastest eval: total=0.8804, eval_time≈45.7min, best user1/rec1. It is useful as a base, but rec2/world dropped.", + "v27": "v12 product/ad repair kept user2/world relatively better but was slow and template-heavy: total=0.8563, eval_time≈70.6min. Do not continue this exact direction.", + "v16": "CoT pattern rewrite failed: total=0.7912; logs show malformed user JSON and fragmented recommendation reasoning.", + "v18": "low-LR mixed replay from v12 failed: total=0.8340; item/world dropped and rec outputs mixed text/itemic/think tags." + }, + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "7375c7a06ae5221c98bc2135819e0999d80e2b2098543ac9a5c56c352bd693d3", + "deadline": "none", + "reproduce": { + "prepare_command": "EXPERIMENT_PREPARE_ONLY=1 python3 experiments/run_experiments.py", + "train_command": "CUDA_VISIBLE_DEVICES= bash -lc 'source /home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/activate && llamafactory-cli train /home/ll/llm4rec/experiments/configs/v29_v19_user_world_guard_lr8e7_ep018.yaml'" + } +} \ 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..e33b3a6 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a467e13883a7304351d69eca750bf7ce6596e006a78e75a8f318a798951f97f2 +size 1602902832 diff --git a/runs/Jul12_04-26-28_ubuntu-System-Product-Name/events.out.tfevents.1783801588.ubuntu-System-Product-Name.776847.0 b/runs/Jul12_04-26-28_ubuntu-System-Product-Name/events.out.tfevents.1783801588.ubuntu-System-Product-Name.776847.0 new file mode 100644 index 0000000..97caec8 --- /dev/null +++ b/runs/Jul12_04-26-28_ubuntu-System-Product-Name/events.out.tfevents.1783801588.ubuntu-System-Product-Name.776847.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:749f54300e1991aadf79c142cd8c13ba8cfcba5bfb6bb5436294cc645c017404 +size 8167 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..c619fc8 --- /dev/null +++ b/train.log @@ -0,0 +1,2270 @@ +/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|2026-07-12 04:25:58] 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] 2026-07-12 04:25:58,574 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055/config.json +[INFO|configuration_utils.py:847] 2026-07-12 04:25:58,577 >> 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] 2026-07-12 04:25:59,252 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055/config.json +[INFO|configuration_utils.py:847] 2026-07-12 04:25:59,253 >> 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] 2026-07-12 04:25:59,253 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055/config.json +[INFO|configuration_utils.py:847] 2026-07-12 04:25:59,253 >> 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|2026-07-12 04:25:59] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v19_user_world_guard_no_template.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: 11510 examples [00:00, 103866.98 examples/s] Generating train split: 18091 examples [00:00, 112750.14 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/18091 [00:00user +【用户交互历史】： +【2022-06-10】 + --:-- [商品-购买] <|prod_begin|> +【2022-06-22】 + --:-- [商品-购买] <|prod_begin|> +【2022-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2022-08-22】 + --:-- [商品-购买] <|prod_begin|> +【2022-12-09】 + --:-- [商品-购买] <|prod_begin|> +【2023-01-04】 + --:-- [商品-购买] <|prod_begin|> +【2023-02-07】 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[视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:59 [视频-点赞/长播] <|video_begin|> +【2026-02-05】 + 00:00 [视频-点赞/长播] <|video_begin|> + 00:10 [视频-点赞/长播] <|video_begin|> + 00:13 [视频-点赞/长播] <|video_begin|> + 01:18 [视频-点赞/长播] <|video_begin|> + 01:18 [视频-点赞/长播] <|video_begin|> + 01:22 [视频-点赞/长播] <|video_begin|> + 01:30 [广告-点击] <|ad_begin|> + 01:34 [视频-点赞/长播] <|video_begin|> + 03:52 [视频-点赞/长播] <|video_begin|> + 05:09 [视频-点赞/长播] <|video_begin|> + 05:09 [视频-评论/长播] <|video_begin|> + 05:11 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-点赞/长播] <|video_begin|> + 05:30 [视频-点赞/长播] <|video_begin|> + 05:36 [视频-点赞/长播] <|video_begin|> + 05:41 [视频-点赞/长播] <|video_begin|> + 05:46 [视频-点赞/长播] <|video_begin|> + 05:49 [视频-点赞/长播] <|video_begin|> + 05:49 [视频-点赞/长播] <|video_begin|> + 05:51 [视频-点赞/长播] <|video_begin|> + 05:51 [视频-点赞/长播] <|video_begin|> + 05:52 [视频-点赞/长播] <|video_begin|> + 05:53 [视频-点赞/长播] <|video_begin|> + 05:53 [视频-点赞/长播] <|video_begin|> + 05:53 [视频-点赞/长播] <|video_begin|> + 05:55 [视频-点赞/长播] <|video_begin|> + 10:00 [视频-点赞/长播] <|video_begin|> + 10:03 [视频-点赞/长播] <|video_begin|> + 10:20 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:26 [视频-长播] <|video_begin|> + 10:28 [视频-长播] <|video_begin|> + 10:28 [视频-点赞/长播] <|video_begin|> + 10:30 [视频-长播] <|video_begin|> + 10:30 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:46 [视频-长播] <|video_begin|> + 10:46 [视频-长播] <|video_begin|> + 10:46 [视频-点赞/长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-收藏/长播] <|video_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 13:03 [视频-长播] <|video_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:08 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> +角色任务：你作为极端严苛的用户行为数据挖掘专家和数据格式化专家，需要根据以上用户交互历史，围绕给定主题抽取存在行为递进逻辑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）： 广告-点击、广告-深度转化、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 +核心抽取逻辑（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 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 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：从被动接收信息到主动学习专业技能的认知升级 + +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学习专业技能的认知升级", + "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":"从被动接收信息到主动学习专业技能的认知升级","events":[{"date":"2025-12-19","action":"[广告-点击] <|ad_begin|>","logic":"初始触发：被动接收到电焊培训广告，对该职业技能产生初步认知。"},{"date":"2026-01-25","action":"[视频-收藏/点赞/长播] <|video_begin|>","logic":"认知深化与主动探索：从宽泛的电焊概念，聚焦到具体的“钣金展开”与“手工放样”等专业技术，并通过收藏/点赞/长播等深度交互行为，表明其兴趣已从被动了解转为主动学习。"},{"date":"2026-01-27","action":"[视频-收藏/点赞/长播] <|video_begin|>","logic":"技能收敛与参数化学习：兴趣进一步聚焦于更具体的“弯头切割”技术，并开始关注“公式”、“图表”、“弧长计算”等量化参数，标志着认知从感性认知进入到需要精确计算的专业技能学习阶段，认知增量显著。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-26】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-01-27】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-02-13】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-04-09】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-12】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-14】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-23】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-05】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-07】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-20】 + --:-- [直播-首次打赏] <|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|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-08】 + --:-- [直播-打赏] <|living_begin|> +【2025-06-09】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-05】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-19】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-04】 + --:-- [直播-打赏] <|living_begin|> +【2025-08-10】 + --:-- [直播-关注] <|living_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-09-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [直播-关注] <|living_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [直播-关注] <|living_begin|> +【2025-09-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> +【2025-11-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-16】 + --:-- [直播-关注] <|living_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_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|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-12】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-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|> +【2025-12-24】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + 10:11 [广告-深度转化] <|ad_begin|> + 10:21 [广告-深度转化] <|ad_begin|> + 16:42 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> + 22:21 [广告-深度转化] <|ad_begin|> + 23:08 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 20:21 [广告-深度转化] <|ad_begin|> + 21:03 [广告-深度转化] <|ad_begin|> + 21:22 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 23:43 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 20:24 [广告-深度转化] <|ad_begin|> + 20:45 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 03:23 [广告-深度转化] <|ad_begin|> + 04:45 [广告-深度转化] <|ad_begin|> + 07:04 [广告-深度转化] <|ad_begin|> + 07:07 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + 12:30 [广告-深度转化] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 00:51 [广告-深度转化] <|ad_begin|> + 21:36 [广告-深度转化] <|ad_begin|> + 23:55 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + 00:20 [广告-深度转化] <|ad_begin|> + 22:20 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 04:44 [广告-深度转化] <|ad_begin|> + 23:38 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + 02:12 [广告-深度转化] <|ad_begin|> + 06:46 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + 15:36 [广告-深度转化] <|ad_begin|> + 23:10 [广告-深度转化] <|ad_begin|> + 23:28 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + 23:18 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 22:09 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 05:03 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 14:00 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 15:30 [广告-深度转化] <|ad_begin|> + 15:50 [广告-深度转化] <|ad_begin|> + 22:46 [广告-深度转化] <|ad_begin|> + 23:48 [广告-深度转化] <|ad_begin|> + 23:59 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 12:41 [广告-深度转化] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 21:00 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:33 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 16:44 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 00:23 [广告-深度转化] <|ad_begin|> + 00:47 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:36 [广告-点击] <|ad_begin|> + 07:40 [广告-点击] <|ad_begin|> + 07:42 [广告-点击] <|ad_begin|> + 23:50 [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|> + 22:47 [视频-关注/长播] <|video_begin|> + 22:47 [视频-关注/长播] <|video_begin|> + 23:15 [视频-关注/点赞/长播] <|video_begin|> + 23:34 [视频-点赞/长播] <|video_begin|> + 23:34 [视频-关注/评论/点赞/长播] <|video_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 06:44 [视频-关注/评论/点赞/长播] <|video_begin|> + 06:47 [视频-长播] <|video_begin|> + 11:37 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 14:27 [广告-点击] <|ad_begin|> + 17:30 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:58 [视频-长播] <|video_begin|> + 23:58 [视频-长播] <|video_begin|> + 23:58 [视频-关注/评论/长播] <|video_begin|> +【2026-01-23】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:12 [视频-关注/评论/点赞/长播] <|video_begin|> + 00:32 [广告-深度转化] <|ad_begin|> + 00:32 [广告-点击] <|ad_begin|> + 00:56 [广告-深度转化] <|ad_begin|> + 00:56 [广告-点击] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:30 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:17 [视频-长播] <|video_begin|> + 14:41 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + 00:04 [广告-深度转化] <|ad_begin|> + 00:04 [广告-点击] <|ad_begin|> + 00:33 [广告-点击] <|ad_begin|> + 00:41 [广告-点击] <|ad_begin|> + 00:47 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 04:09 [广告-深度转化] <|ad_begin|> + 04:09 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:04 [广告-点击] <|ad_begin|> +【2026-01-26】 + 00:01 [广告-点击] <|ad_begin|> + 00:07 [广告-深度转化] <|ad_begin|> + 00:07 [广告-点击] <|ad_begin|> + 04:51 [视频-长播] <|video_begin|> + 04:51 [视频-长播] <|video_begin|> + 04:55 [视频-长播] <|video_begin|> + 04:55 [视频-点赞/长播] <|video_begin|> + 05:18 [视频-长播] <|video_begin|> + 05:18 [视频-关注/长播] <|video_begin|> + 05:39 [广告-深度转化] <|ad_begin|> + 05:39 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:14 [视频-长播] <|video_begin|> + 22:55 [广告-深度转化] <|ad_begin|> + 22:55 [广告-点击] <|ad_begin|> + 22:58 [广告-点击] <|ad_begin|> + 23:04 [视频-长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 00:15 [广告-点击] <|ad_begin|> + 00:27 [广告-深度转化] <|ad_begin|> + 00:27 [广告-点击] <|ad_begin|> + 00:30 [广告-点击] <|ad_begin|> + 00:39 [广告-点击] <|ad_begin|> + 01:06 [广告-点击] <|ad_begin|> + 01:16 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:30 [广告-深度转化] <|ad_begin|> + 15:30 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + 07:47 [广告-深度转化] <|ad_begin|> + 07:47 [广告-点击] <|ad_begin|> + 22:49 [广告-点击] <|ad_begin|> + 22:53 [广告-深度转化] <|ad_begin|> + 22:53 [广告-点击] <|ad_begin|> + 23:16 [广告-点击] <|ad_begin|> + 23:16 [广告-点击] <|ad_begin|> +【2026-01-29】 + --:-- [直播-关注] <|living_begin|> + 00:50 [广告-深度转化] <|ad_begin|> + 00:50 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:11 [广告-点击] <|ad_begin|> + 07:11 [视频-长播] <|video_begin|> + 07:11 [视频-关注/长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 07:37 [视频-长播] <|video_begin|> + 07:37 [视频-关注/长播] <|video_begin|> + 07:39 [广告-点击] <|ad_begin|> + 07:44 [广告-点击] <|ad_begin|> + 14:56 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-关注/长播] <|video_begin|> + 15:24 [广告-深度转化] <|ad_begin|> + 15:24 [广告-点击] <|ad_begin|> + 15:34 [广告-点击] <|ad_begin|> + 19:27 [视频-长播] <|video_begin|> + 19:27 [视频-关注/点赞/长播] <|video_begin|> + 19:27 [视频-点赞/长播] <|video_begin|> + 19:46 [视频-长播] <|video_begin|> + 19:46 [视频-关注/长播] <|video_begin|> + 19:57 [视频-长播] <|video_begin|> + 22:04 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 05:43 [视频-长播] <|video_begin|> + 05:43 [视频-关注/评论/点赞/长播] <|video_begin|> + 06:25 [广告-点击] <|ad_begin|> + 06:30 [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|> + 10:16 [视频-长播] <|video_begin|> + 10:24 [广告-点击] <|ad_begin|> + 10:29 [广告-点击] <|ad_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + 10:37 [广告-深度转化] <|ad_begin|> + 10:37 [广告-点击] <|ad_begin|> + 10:45 [广告-深度转化] <|ad_begin|> + 10:45 [广告-点击] <|ad_begin|> +【2026-01-31】 + 00:11 [广告-深度转化] <|ad_begin|> + 00:11 [广告-点击] <|ad_begin|> + 00:30 [广告-点击] <|ad_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-转发/点赞/长播] <|video_begin|> + 08:27 [广告-点击] <|ad_begin|> + 08:37 [视频-关注/长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 12:45 [广告-深度转化] <|ad_begin|> + 12:45 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 11:45 [视频-长播] <|video_begin|> + 11:45 [视频-长播] <|video_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 12:25 [广告-点击] <|ad_begin|> + 13:58 [广告-深度转化] <|ad_begin|> + 13:58 [广告-点击] <|ad_begin|> + 18:17 [广告-点击] <|ad_begin|> + 18:17 [广告-点击] <|ad_begin|> + 18:28 [广告-深度转化] <|ad_begin|> + 18:28 [广告-点击] <|ad_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:59 [广告-点击] <|ad_begin|> + 08:04 [广告-深度转化] <|ad_begin|> + 08:04 [广告-点击] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 08:16 [广告-点击] <|ad_begin|> + 13:08 [视频-长播] <|video_begin|> + 13:08 [视频-关注/点赞/长播] <|video_begin|> + 23:48 [视频-长播] <|video_begin|> + 23:48 [视频-关注/长播] <|video_begin|> + 23:51 [视频-关注/点赞/长播] <|video_begin|> + 23:51 [视频-点赞/长播] <|video_begin|> + 23:57 [广告-点击] <|ad_begin|> + 23:57 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:29 [广告-点击] <|ad_begin|> + 01:13 [视频-关注/评论/长播] <|video_begin|> + 01:18 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:15 [视频-长播] <|video_begin|> + 07:30 [广告-点击] <|ad_begin|> + 07:32 [视频-长播] <|video_begin|> + 07:36 [广告-点击] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 07:39 [广告-点击] <|ad_begin|> + 18:07 [视频-长播] <|video_begin|> + 18:07 [视频-关注/点赞/长播] <|video_begin|> + 18:19 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 07:14 [广告-点击] <|ad_begin|> + 07:54 [视频-长播] <|video_begin|> + 07:55 [视频-长播] <|video_begin|> + 07:55 [视频-关注/长播] <|video_begin|> + 07:55 [视频-关注/长播] <|video_begin|> + 08:07 [视频-长播] <|video_begin|> + 08:07 [视频-长播] <|video_begin|> + 08:07 [视频-评论/长播] <|video_begin|> + 13:12 [广告-点击] <|ad_begin|> + 13:16 [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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:05 [视频-关注/长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:41 [广告-点击] <|ad_begin|> + 13:41 [广告-点击] <|ad_begin|> + 13:47 [视频-长播] <|video_begin|> + 13:47 [视频-长播] <|video_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-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：从泛化健康焦虑到聚焦特定成分的保健品探索 + +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|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|ad_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-11-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-08】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [直播-关注] <|living_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-10】 + --:-- [直播-关注] <|living_begin|> +【2025-01-22】 + --:-- [直播-关注] <|living_begin|> +【2025-01-31】 + --:-- [直播-关注] <|living_begin|> +【2025-02-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-06】 + --:-- [直播-关注] <|living_begin|> +【2025-02-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-02-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-02-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-01】 + --:-- [直播-关注] <|living_begin|> +【2025-03-08】 + --:-- [直播-关注] <|living_begin|> +【2025-03-10】 + --:-- [直播-关注] <|living_begin|> +【2025-03-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-20】 + --:-- [直播-关注] <|living_begin|> +【2025-03-21】 + --:-- [直播-关注] <|living_begin|> +【2025-03-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-23】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-28】 + --:-- [直播-关注] <|living_begin|> +【2025-04-09】 + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-20】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-06】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-19】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-28】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-06-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-06-04】 + --:-- [直播-关注] <|living_begin|> +【2025-06-07】 + --:-- [直播-关注/打赏] <|living_begin|> +【2025-06-17】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-06-23】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注] <|living_begin|> +【2025-07-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-05】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-06】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-11】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-07】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-26】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [直播-关注] <|living_begin|> +【2025-11-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-02】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-08】 + 09:06 [广告-深度转化] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 09:32 [广告-深度转化] <|ad_begin|> + 09:32 [广告-点击] <|ad_begin|> + 23:01 [广告-点击] <|ad_begin|> +【2025-12-10】 + 10:25 [广告-深度转化] <|ad_begin|> + 10:25 [广告-点击] <|ad_begin|> +【2025-12-11】 + 10:27 [广告-深度转化] <|ad_begin|> + 10:27 [广告-点击] <|ad_begin|> +【2025-12-12】 + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:28 [广告-深度转化] <|ad_begin|> + 04:28 [广告-点击] <|ad_begin|> + 04:47 [广告-深度转化] <|ad_begin|> + 04:47 [广告-点击] <|ad_begin|> + 08:21 [广告-点击] <|ad_begin|> + 09:20 [广告-深度转化] <|ad_begin|> + 09:20 [广告-点击] <|ad_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|> +【2025-12-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-14】 + --:-- [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|> +【2025-12-17】 + 08:03 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + 06:55 [广告-深度转化] <|ad_begin|> + 06:55 [广告-点击] <|ad_begin|> +【2025-12-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-26】 + 16:28 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-28】 + 00:54 [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|> +【2025-12-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-01】 + 00:56 [广告-点击] <|ad_begin|> + 01:36 [广告-深度转化] <|ad_begin|> + 01:36 [广告-点击] <|ad_begin|> + 04:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:15 [广告-深度转化] <|ad_begin|> + 16:15 [广告-点击] <|ad_begin|> +【2026-01-02】 + 03:11 [广告-点击] <|ad_begin|> + 03:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + 02:07 [广告-深度转化] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 12:48 [广告-深度转化] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-04】 + 09:35 [广告-深度转化] <|ad_begin|> + 09:35 [广告-点击] <|ad_begin|> + 10:06 [广告-深度转化] <|ad_begin|> + 10:06 [广告-点击] <|ad_begin|> +【2026-01-05】 + 08:45 [广告-深度转化] <|ad_begin|> + 08:45 [广告-点击] <|ad_begin|> + 09:15 [广告-深度转化] <|ad_begin|> + 09:15 [广告-点击] <|ad_begin|> +【2026-01-06】 + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> +【2026-01-07】 + 08:35 [广告-点击] <|ad_begin|> + 08:38 [广告-深度转化] <|ad_begin|> + 08:38 [广告-点击] <|ad_begin|> + 15:53 [广告-深度转化] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> + 15:54 [广告-点击] <|ad_begin|> +【2026-01-08】 + 09:14 [广告-深度转化] <|ad_begin|> + 09:14 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 08:10 [广告-深度转化] <|ad_begin|> + 08:10 [广告-点击] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 09:25 [广告-深度转化] <|ad_begin|> + 09:25 [广告-点击] <|ad_begin|> + 09:49 [广告-深度转化] <|ad_begin|> + 09:49 [广告-点击] <|ad_begin|> + 10:26 [广告-点击] <|ad_begin|> + 10:51 [广告-深度转化] <|ad_begin|> + 10:51 [广告-点击] <|ad_begin|> +【2026-01-10】 + 00:55 [广告-深度转化] <|ad_begin|> + 00:55 [广告-点击] <|ad_begin|> + 01:26 [广告-深度转化] <|ad_begin|> + 01:26 [广告-点击] <|ad_begin|> + 01:29 [广告-深度转化] <|ad_begin|> + 01:29 [广告-点击] <|ad_begin|> + 04:12 [广告-点击] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + 01:10 [广告-深度转化] <|ad_begin|> + 01:10 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-16】 + 09:28 [广告-深度转化] <|ad_begin|> + 09:28 [广告-点击] <|ad_begin|> +【2026-01-18】 + 06:21 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:12 [广告-点击] <|ad_begin|> +【2026-01-20】 + 09:20 [广告-点击] <|ad_begin|> + 23:10 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:55 [视频-长播] <|video_begin|> +【2026-01-22】 + 23:02 [视频-长播] <|video_begin|> + 23:02 [视频-长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-24】 + 13:48 [视频-长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 05:05 [广告-点击] <|ad_begin|> + 08:12 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:52 [视频-长播] <|video_begin|> + 22:52 [视频-长播] <|video_begin|> +【2026-01-26】 + 05:54 [广告-点击] <|ad_begin|> + 12:39 [广告-深度转化] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 10:54 [视频-长播] <|video_begin|> + 10:54 [视频-长播] <|video_begin|> + 22:51 [视频-长播] <|video_begin|> + 22:59 [广告-点击] <|ad_begin|> +【2026-01-29】 + 23:01 [广告-点击] <|ad_begin|> + 23:10 [广告-点击] <|ad_begin|> + 23:15 [广告-点击] <|ad_begin|> +【2026-01-30】 + 14:42 [广告-点击] <|ad_begin|> + 14:50 [视频-长播] <|video_begin|> + 14:50 [视频-长播] <|video_begin|> + 15:06 [视频-长播] <|video_begin|> +【2026-01-31】 + 14:13 [视频-长播] <|video_begin|> + 15:20 [广告-深度转化] <|ad_begin|> + 15:20 [广告-点击] <|ad_begin|> + 23:35 [视频-长播] <|video_begin|> +【2026-02-01】 + 05:55 [广告-点击] <|ad_begin|> + 14:26 [广告-深度转化] <|ad_begin|> + 14:26 [广告-点击] <|ad_begin|> + 14:27 [广告-点击] <|ad_begin|> +【2026-02-02】 + 09:00 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> + 21:58 [视频-长播] <|video_begin|> + 22:16 [视频-长播] <|video_begin|> + 22:16 [视频-长播] <|video_begin|> +【2026-02-03】 + 09:15 [视频-长播] <|video_begin|> + 13:49 [视频-长播] <|video_begin|> + 13:49 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-04】 + 05:57 [广告-点击] <|ad_begin|> + 12:44 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> +【2026-02-05】 + 07:56 [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）： 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）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +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 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。 +请针对下列主题提取行为逻辑链： +主题：从被动接收到主动探索的烹饪技能学习路径 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收到主动探索的烹饪技能学习路径", + "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数据，以推断其在烹饪技能学习上的行为路径。 + +从整体时间线来看，我注意到用户的行为模式在2026年1月下旬出现了一个有趣的变化，特别是在烹饪这个主题上。最初，用户的购物行为主要集中在生活日用品、零食、服饰以及一些与家庭教育相关的商品上，并没有表现出对烹饪过程本身的强烈兴趣。然而，一个细微的兴趣点似乎在1月21日被点燃了。 + +那天，我发现用户点击了一款儿童也能食用的山楂糖醋汁 <|prod_begin|>。这个行为本身很普通，但它揭示了一个潜在的需求：用户想要制作糖醋口味的菜肴，比如糖醋里脊或排骨，并且可能是为了满足家庭成员（特别是孩子）的口味。此时，用户的解决方案非常直接，倾向于购买现成的调味酱汁，这是一种典型的“被动接收”或寻求便捷方案的模式，希望通过成品快速达到烹饪目标，而不是深入学习制作过程。 + +然而，仅仅几天后，用户的行为发生了显著的转变。在1月25日，我看到用户观看了一个详细教学如何制作糖醋里脊和糖醋排骨的视频 <|video_begin|>。这个视频的核心内容之一就是“糖醋汁调制”。这表明，用户可能对之前考虑购买的成品糖醋汁效果不满意，或者在使用过程中遇到了问题，也可能是在浏览相关内容时，好奇心被激发，从而转向了“主动探索”的模式。用户不再满足于仅仅使用成品调料，而是开始希望掌握这道菜的核心技术，即如何亲手调制出完美的糖醋汁。这是一个关键的认知升级，标志着用户从一个单纯的烹饪执行者，向一个技能学习者转变。 + +紧接着，这种对烹饪技巧的探索欲似乎被进一步拓宽和深化了。就在前一天，也就是1月24日，用户还观看了一个关于如何制作美味饺子的视频 <|video_begin|>。这个视频的重点是讲解饺子馅调味的“四不放”与“五放”技巧。我推测，当用户成功掌握或理解了“糖醋汁”这种单一、目标明确的调味方法后，其信心和兴趣得到了增强，开始向更复杂、更讲究经验和配比的复合调味领域拓展。饺子馅的调味显然比单一的糖醋汁要复杂得多，涉及到多种食材和调料的平衡。这个行为表明，用户的烹饪学习已经从解决单一菜品的需求，泛化到了对中式烹饪中更底层、更通用的调味原理的探究。 + +综合来看，我认为这位用户的烹饪技能学习路径体现了一个清晰的递进关系。整个过程始于一个具体的烹饪需求，最初尝试通过最简单的方式解决，但随后兴趣被激发，转而主动学习核心技巧，并最终将这种学习热情泛化到更广泛的烹饪知识领域。因此，我倾向于认为，用户对烹饪的兴趣正在增长，未来可能会继续关注更深入的烹饪教程、食材选购以及厨房用具等相关内容，有较大概率被此类推荐内容所吸引。 + +我将这个行为逻辑链总结如下：用户的烹饪兴趣最初由对糖醋类菜品的需求触发，表现为点击购买便捷的糖醋汁 <|prod_begin|>。随后，用户不满足于成品调料，开始主动学习核心烹饪技巧，观看了关于糖醋里脊和排骨制作的教学视频 <|video_begin|>，重点关注糖醋汁的调制方法。在掌握了单一调味技巧后，用户的兴趣进一步泛化和深化，开始学习更复杂的复合调味知识，例如观看关于饺子馅调味技巧的视频 <|video_begin|>。 +{"logic_chain": {"name": "从被动接收到主动探索的烹饪技能学习路径", "events": [{"date": "2026-01-21", "action": "[商品-点击] <|prod_begin|>", "logic": "问题触发：对“糖醋”类菜品产生兴趣，但初始行为是寻求成品的“糖醋汁”，试图通过购买便捷调料来解决烹饪问题。"}, {"date": "2026-01-25", "action": "[视频-长播] <|video_begin|>", "logic": "行为转变与认知升级：从依赖成品调料转向主动学习核心技能。观看教学视频，重点关注了前期商品中涉及的“糖醋汁调制”方法，表明用户不满足于便捷方案，开始探究底层原理。"}, {"date": "2026-01-24", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣泛化与深化：在掌握了“糖醋汁”这一特定调味技巧后，用户的烹饪兴趣扩展到更复杂的调味领域——饺子馅的“四不放”与“五放”，这代表了从单一菜品调味到复合型调味技巧的认知深化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-09-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-26】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-27】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-29】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [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|> +【2026-01-01】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-03】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + 10:50 [广告-深度转化] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 15:17 [广告-深度转化] <|ad_begin|> + 15:55 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + 02:09 [广告-深度转化] <|ad_begin|> + 02:13 [广告-深度转化] <|ad_begin|> + 02:40 [广告-深度转化] <|ad_begin|> + 04:18 [广告-深度转化] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-08】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-09】 + --:-- [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|> + 11:32 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 10:57 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-14】 + --:-- [直播-关注/打赏] <|living_begin|> +【2026-01-15】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> +【2026-01-18】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-19】 + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-20】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-21】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 10:11 [广告-深度转化] <|ad_begin|> + 10:18 [广告-深度转化] <|ad_begin|> + 12:19 [广告-深度转化] <|ad_begin|> + 12:20 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:31 [广告-深度转化] <|ad_begin|> + 13:38 [广告-深度转化] <|ad_begin|> + 13:39 [广告-深度转化] <|ad_begin|> + 13:43 [广告-深度转化] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:08 [广告-深度转化] <|ad_begin|> + 14:17 [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|> + 14:44 [广告-深度转化] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:09 [广告-深度转化] <|ad_begin|> + 15:10 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + 01:29 [广告-深度转化] <|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|> + 04:32 [广告-深度转化] <|ad_begin|> + 04:36 [广告-深度转化] <|ad_begin|> + 04:36 [广告-深度转化] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 17:20 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|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|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 08:26 [视频-长播] <|video_begin|> + 11:42 [广告-深度转化] <|ad_begin|> + 13:27 [视频-长播] <|video_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + --:-- [直播-关注/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|> + 07:38 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 10:39 [广告-深度转化] <|ad_begin|> + 11:44 [广告-深度转化] <|ad_begin|> + 15:57 [广告-深度转化] <|ad_begin|> + 15:58 [广告-深度转化] <|ad_begin|> + 15:59 [广告-深度转化] <|ad_begin|> + 17:12 [视频-长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:12 [视频-长播] <|video_begin|> + 00:43 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:55 [广告-深度转化] <|ad_begin|> + 03:09 [视频-长播] <|video_begin|> + 03:33 [视频-长播] <|video_begin|> + 03:33 [视频-长播] <|video_begin|> + 05:20 [广告-深度转化] <|ad_begin|> + 10:54 [广告-深度转化] <|ad_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 12:37 [视频-长播] <|video_begin|> + 13:48 [广告-深度转化] <|ad_begin|> + 14:17 [广告-深度转化] <|ad_begin|> + 14:19 [广告-深度转化] <|ad_begin|> + 14:20 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + 01:01 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:55 [视频-长播] <|video_begin|> + 21:55 [视频-长播] <|video_begin|> +【2026-01-27】 + --:-- [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-点击] <|prod_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 03:50 [视频-长播] <|video_begin|> + 04:32 [视频-长播] <|video_begin|> + 04:58 [广告-深度转化] <|ad_begin|> + 10:16 [广告-深度转化] <|ad_begin|> + 10:28 [视频-长播] <|video_begin|> +【2026-01-29】 + --:-- [直播-首次打赏] <|living_begin|> + 01:18 [视频-长播] <|video_begin|> + 01:18 [视频-长播] <|video_begin|> + 08:01 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注/打赏] <|living_begin|> + 03:38 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:53 [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|> + 07:16 [视频-长播] <|video_begin|> + 08:27 [视频-长播] <|video_begin|> + 08:27 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:23 [视频-长播] <|video_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:39 [视频-长播] <|video_begin|> + 17:01 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:33 [广告-深度转化] <|ad_begin|> + 13:57 [视频-长播] <|video_begin|> + 13:57 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:30 [视频-长播] <|video_begin|> + 00:30 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:47 [视频-长播] <|video_begin|> + 15:13 [广告-深度转化] <|ad_begin|> + 15:14 [广告-深度转化] <|ad_begin|> + 16:06 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [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|> + 01:19 [视频-长播] <|video_begin|> + 01:19 [视频-长播] <|video_begin|> + 08:54 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 09:00 [广告-深度转化] <|ad_begin|> + 11:16 [广告-深度转化] <|ad_begin|> + 13:40 [广告-深度转化] <|ad_begin|> + 15:59 [广告-深度转化] <|ad_begin|> + 16:21 [视频-长播] <|video_begin|> + 16:21 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:43 [广告-点击] <|ad_begin|> + 00:57 [广告-深度转化] <|ad_begin|> + 00:57 [广告-点击] <|ad_begin|> + 01:02 [广告-点击] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 01:08 [广告-点击] <|ad_begin|> + 01:09 [广告-点击] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 01:33 [广告-点击] <|ad_begin|> + 01:50 [广告-点击] <|ad_begin|> + 01:58 [广告-点击] <|ad_begin|> + 02:00 [广告-点击] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 04:03 [广告-点击] <|ad_begin|> + 04:04 [广告-点击] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 04:43 [广告-点击] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 04:46 [广告-深度转化] <|ad_begin|> + 04:46 [广告-点击] <|ad_begin|> + 04:47 [广告-点击] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:50 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:12 [广告-点击] <|ad_begin|> + 05:13 [广告-点击] <|ad_begin|> + 05:15 [广告-点击] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 07:31 [广告-点击] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 08:32 [广告-点击] <|ad_begin|> + 09:03 [广告-点击] <|ad_begin|> + 09:05 [广告-点击] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + 09:13 [广告-点击] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 09:48 [视频-长播] <|video_begin|> + 10:15 [广告-点击] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> + 11:08 [广告-点击] <|ad_begin|> + 11:12 [广告-点击] <|ad_begin|> + 11:14 [广告-点击] <|ad_begin|> + 11:15 [广告-深度转化] <|ad_begin|> + 11:15 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:17 [广告-点击] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:35 [广告-点击] <|ad_begin|> + 11:42 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 11:57 [广告-点击] <|ad_begin|> + 12:01 [广告-点击] <|ad_begin|> + 12:03 [广告-点击] <|ad_begin|> + 12:10 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:21 [广告-点击] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 14:27 [广告-点击] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 15:15 [广告-点击] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> + 15:59 [广告-点击] <|ad_begin|> + 15:59 [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|> +角色任务：你是极端严苛的用户行为数据挖掘专家与数据格式化专家，请依据以上用户交互历史，围绕给定主题提取具备递进关系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）： 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）： +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 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：从娱乐消遣到变现探索的认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从娱乐消遣到变现探索的认知演化", + "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":"从娱乐消遣到变现探索的认知演化","events":[{"date":"2025-12-15","action":"[直播-首次打赏] <|living_begin|>","logic":"初始阶段：用户行为以典型的娱乐消费为主，关注并打赏高颜值、才艺型女主播，属于被动内容消费。"},{"date":"2025-12-29","action":"[直播-首次打赏] <|living_begin|>","logic":"认知跃迁：用户首次打赏一个“不露脸”的教学型直播，其核心内容是“金币赚取攻略”和“账号权重科普”。这标志着用户从纯粹的观众，转变为对平台玩法和变现机制产生兴趣的探索者，实现了从“消费”到“生产/获利”的认知转变。"},{"date":"2026-01-08","action":"[直播-首次打赏] <|living_begin|>","logic":"行为强化与深化：用户再次打赏同类型的“快手极速版干货分享”直播，进一步关注“停留互动技巧”等更深层次的玩法。这表明其对该领域的兴趣得到确认并持续深化，正在主动学习如何更有效地参与平台活动。"}]}}<|im_end|> +<|im_start|>user +你是广告语义到token的映射助手，请阅读广告描述并生成准确的广告token。 +请根据这段广告dense 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-100, -100, -100, -100, -100, 151667, 198, 107439, 53481, 101047, 100185, 107888, 5373, 79256, 5373, 37029, 102122, 33108, 102248, 3837, 106188, 31235, 104469, 9370, 101927, 72881, 64559, 106918, 8997, 151668, 198, 176251, 156209, 164659, 175728, 151645, 198, -100][INFO|configuration_utils.py:769] 2026-07-12 04:26:27,817 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055/config.json +[INFO|configuration_utils.py:847] 2026-07-12 04:26:27,819 >> 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 +} + + +labels: +{"logic_chain":{"name":"从被动接收信息到主动学习专业技能的认知升级","events":[{"date":"2025-12-19","action":"[广告-点击] <|ad_begin|>","logic":"初始触发：被动接收到电焊培训广告，对该职业技能产生初步认知。"},{"date":"2026-01-25","action":"[视频-收藏/点赞/长播] <|video_begin|>","logic":"认知深化与主动探索：从宽泛的电焊概念，聚焦到具体的“钣金展开”与“手工放样”等专业技术，并通过收藏/点赞/长播等深度交互行为，表明其兴趣已从被动了解转为主动学习。"},{"date":"2026-01-27","action":"[视频-收藏/点赞/长播] <|video_begin|>","logic":"技能收敛与参数化学习：兴趣进一步聚焦于更具体的“弯头切割”技术，并开始关注“公式”、“图表”、“弧长计算”等量化参数，标志着认知从感性认知进入到需要精确计算的专业技能学习阶段，认知增量显著。"}]}}<|im_end|> +["<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|video_begin|>","<|ad_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> + +好的，我来分析一下这位用户的行为数据，以推断其在烹饪技能学习上的行为路径。 + +从整体时间线来看，我注意到用户的行为模式在2026年1月下旬出现了一个有趣的变化，特别是在烹饪这个主题上。最初，用户的购物行为主要集中在生活日用品、零食、服饰以及一些与家庭教育相关的商品上，并没有表现出对烹饪过程本身的强烈兴趣。然而，一个细微的兴趣点似乎在1月21日被点燃了。 + +那天，我发现用户点击了一款儿童也能食用的山楂糖醋汁 <|prod_begin|>。这个行为本身很普通，但它揭示了一个潜在的需求：用户想要制作糖醋口味的菜肴，比如糖醋里脊或排骨，并且可能是为了满足家庭成员（特别是孩子）的口味。此时，用户的解决方案非常直接，倾向于购买现成的调味酱汁，这是一种典型的“被动接收”或寻求便捷方案的模式，希望通过成品快速达到烹饪目标，而不是深入学习制作过程。 + +然而，仅仅几天后，用户的行为发生了显著的转变。在1月25日，我看到用户观看了一个详细教学如何制作糖醋里脊和糖醋排骨的视频 <|video_begin|>。这个视频的核心内容之一就是“糖醋汁调制”。这表明，用户可能对之前考虑购买的成品糖醋汁效果不满意，或者在使用过程中遇到了问题，也可能是在浏览相关内容时，好奇心被激发，从而转向了“主动探索”的模式。用户不再满足于仅仅使用成品调料，而是开始希望掌握这道菜的核心技术，即如何亲手调制出完美的糖醋汁。这是一个关键的认知升级，标志着用户从一个单纯的烹饪执行者，向一个技能学习者转变。 + +紧接着，这种对烹饪技巧的探索欲似乎被进一步拓宽和深化了。就在前一天，也就是1月24日，用户还观看了一个关于如何制作美味饺子的视频 <|video_begin|>。这个视频的重点是讲解饺子馅调味的“四不放”与“五放”技巧。我推测，当用户成功掌握或理解了“糖醋汁”这种单一、目标明确的调味方法后，其信心和兴趣得到了增强，开始向更复杂、更讲究经验和配比的复合调味领域拓展。饺子馅的调味显然比单一的糖醋汁要复杂得多，涉及到多种食材和调料的平衡。这个行为表明，用户的烹饪学习已经从解决单一菜品的需求，泛化到了对中式烹饪中更底层、更通用的调味原理的探究。 + +综合来看，我认为这位用户的烹饪技能学习路径体现了一个清晰的递进关系。整个过程始于一个具体的烹饪需求，最初尝试通过最简单的方式解决，但随后兴趣被激发，转而主动学习核心技巧，并最终将这种学习热情泛化到更广泛的烹饪知识领域。因此，我倾向于认为，用户对烹饪的兴趣正在增长，未来可能会继续关注更深入的烹饪教程、食材选购以及厨房用具等相关内容，有较大概率被此类推荐内容所吸引。 + +我将这个行为逻辑链总结如下：用户的烹饪兴趣最初由对糖醋类菜品的需求触发，表现为点击购买便捷的糖醋汁 <|prod_begin|>。随后，用户不满足于成品调料，开始主动学习核心烹饪技巧，观看了关于糖醋里脊和排骨制作的教学视频 <|video_begin|>，重点关注糖醋汁的调制方法。在掌握了单一调味技巧后，用户的兴趣进一步泛化和深化，开始学习更复杂的复合调味知识，例如观看关于饺子馅调味技巧的视频 <|video_begin|>。 +{"logic_chain": {"name": "从被动接收到主动探索的烹饪技能学习路径", "events": [{"date": "2026-01-21", "action": "[商品-点击] <|prod_begin|>", "logic": "问题触发：对“糖醋”类菜品产生兴趣，但初始行为是寻求成品的“糖醋汁”，试图通过购买便捷调料来解决烹饪问题。"}, {"date": "2026-01-25", "action": "[视频-长播] <|video_begin|>", "logic": "行为转变与认知升级：从依赖成品调料转向主动学习核心技能。观看教学视频，重点关注了前期商品中涉及的“糖醋汁调制”方法，表明用户不满足于便捷方案，开始探究底层原理。"}, {"date": "2026-01-24", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣泛化与深化：在掌握了“糖醋汁”这一特定调味技巧后，用户的烹饪兴趣扩展到更复杂的调味领域——饺子馅的“四不放”与“五放”，这代表了从单一菜品调味到复合型调味技巧的认知深化。"}]}}<|im_end|> +{"logic_chain":{"name":"从娱乐消遣到变现探索的认知演化","events":[{"date":"2025-12-15","action":"[直播-首次打赏] <|living_begin|>","logic":"初始阶段：用户行为以典型的娱乐消费为主，关注并打赏高颜值、才艺型女主播，属于被动内容消费。"},{"date":"2025-12-29","action":"[直播-首次打赏] <|living_begin|>","logic":"认知跃迁：用户首次打赏一个“不露脸”的教学型直播，其核心内容是“金币赚取攻略”和“账号权重科普”。这标志着用户从纯粹的观众，转变为对平台玩法和变现机制产生兴趣的探索者，实现了从“消费”到“生产/获利”的认知转变。"},{"date":"2026-01-08","action":"[直播-首次打赏] <|living_begin|>","logic":"行为强化与深化：用户再次打赏同类型的“快手极速版干货分享”直播，进一步关注“停留互动技巧”等更深层次的玩法。这表明其对该领域的兴趣得到确认并持续深化，正在主动学习如何更有效地参与平台活动。"}]}}<|im_end|> + +提取描述中的核心品类、属性、使用场景和人群，匹配最接近的广告语义标识。 + +<|ad_begin|><|im_end|> + +[INFO|2026-07-12 04:26:27] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-12 04:26:28] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-12 04:26:28,193 >> loading weights file /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-12 04:26:28,193 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-12 04:26:28,350 >> 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/v19_v15_dualmode_fast_lr5e6_ep055/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-12 04:26:28,743 >> 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] 2026-07-12 04:26:28,743 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v19_v15_dualmode_fast_lr5e6_ep055. +[INFO|2026-07-12 04:26:28] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-12 04:26:28] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-12 04:26:28] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-12 04:26:28] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-12 04:26:28] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-12 04:26:28,912 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-12 04:26:28,912 >> Num examples = 1,027 +[INFO|trainer.py:1471] 2026-07-12 04:26:28,912 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-12 04:26:28,912 >> Num update steps per epoch = 257 +[INFO|trainer.py:1473] 2026-07-12 04:26:28,912 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-12 04:26:28,912 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-12 04:26:28,912 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-12 04:26:28,912 >> Total optimization steps = 47 +[INFO|trainer.py:1479] 2026-07-12 04:26:28,912 >> Number of trainable parameters = 801,433,600 + 0%| | 0/47 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 47/47 [06:34<00:00, 8.39s/it] 100%|██████████| 47/47 [06:34<00:00, 8.40s/it] +[INFO|trainer.py:3815] 2026-07-12 04:33:03,847 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018 +[INFO|configuration_utils.py:533] 2026-07-12 04:33:03,848 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:816] 2026-07-12 04:33:03,849 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/generation_config.json +{'loss': '1.598', 'grad_norm': '6.594', 'learning_rate': '7.916e-07', 'epoch': '0.01947'} +{'loss': '1.48', 'grad_norm': '32.75', 'learning_rate': '7.418e-07', 'epoch': '0.03895'} +{'loss': '1.378', 'grad_norm': '8.312', 'learning_rate': '6.524e-07', 'epoch': '0.05842'} +{'loss': '1.375', 'grad_norm': '6.906', 'learning_rate': '5.34e-07', 'epoch': '0.0779'} +{'loss': '1.41', 'grad_norm': '33.5', 'learning_rate': '4e-07', 'epoch': '0.09737'} +{'loss': '1.57', 'grad_norm': '13.12', 'learning_rate': '2.66e-07', 'epoch': '0.1168'} +{'loss': '1.434', 'grad_norm': '13.19', 'learning_rate': '1.476e-07', 'epoch': '0.1363'} +{'loss': '1.53', 'grad_norm': '14.56', 'learning_rate': '5.823e-08', 'epoch': '0.1558'} +{'loss': '1.511', 'grad_norm': '6', 'learning_rate': '8.366e-09', 'epoch': '0.1753'} +{'train_runtime': '394.9', 'train_samples_per_second': '0.468', 'train_steps_per_second': '0.119', 'train_loss': '1.48', 'epoch': '0.1831'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-12 04:33:04,803 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-12 04:33:04,825 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/tokenizer_config.json +***** train metrics ***** + epoch = 0.1831 + total_flos = 20887212GF + train_loss = 1.4801 + train_runtime = 0:06:34.93 + train_samples_per_second = 0.468 + train_steps_per_second = 0.119 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/training_loss.png +[WARNING|2026-07-12 04:33:04] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-12 04:33:04] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-12 04:33:04,921 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} diff --git a/train_results.json b/train_results.json new file mode 100644 index 0000000..6efc793 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 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