From e5a0d25204f5a014fe71720d21a8fc6905432d94 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 7 Aug 2026 11:33:17 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/onereason_0.8b_sft Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + generation_config.json | 12 + model.safetensors | 3 + ...45526.ubuntu-System-Product-Name.1097917.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2355 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 81 + trainer_state.json | 603 +++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 40086 bytes 16 files changed, 3341 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul05_17-58-46_ubuntu-System-Product-Name/events.out.tfevents.1783245526.ubuntu-System-Product-Name.1097917.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs 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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..8bbef16 --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: OpenOneRec/OneReason-0.8B-pretrain-competition +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: onereason_0.8b_sft + results: [] +--- + + + +# onereason_0.8b_sft + +This model is a fine-tuned version of [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the data_final 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: 2e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 20260701 +- 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 + +### 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..8cf90bd --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.9165347193492685e+17, + "train_loss": 1.5697158980369568, + "train_runtime": 3253.1715, + "train_samples_per_second": 0.491, + "train_steps_per_second": 0.123 +} \ 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/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..5871c71 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d3bb000bd65ffd468cbd154366027bb20fee3326c34556c34d5b636dcf17110 +size 1602902832 diff --git a/runs/Jul05_17-58-46_ubuntu-System-Product-Name/events.out.tfevents.1783245526.ubuntu-System-Product-Name.1097917.0 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false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": false, + "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..81a7258 --- /dev/null +++ b/train.log @@ -0,0 +1,2355 @@ +/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|2026-07-05 17:54:35] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:771] 2026-07-05 17:54:35,788 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-05 17:54:35,794 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "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": null, + "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": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-05 17:54:39,168 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-05 17:54:39,171 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "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": null, + "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": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-05 17:54:39,512 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-05 17:54:39,515 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "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": null, + "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": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-05 17:54:41] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/demo/data/data_final.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: 1172 examples [00:00, 9767.22 examples/s] Generating train split: 14237 examples [00:00, 72217.18 examples/s] Generating train split: 29300 examples [00:00, 103845.22 examples/s] Generating train split: 32480 examples [00:00, 92180.57 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/32480 [00:00user +【用户交互历史】： +【2025-01-08】 + --:-- [直播-关注] <|living_begin|> +【2025-01-29】 + --:-- [直播-关注] <|living_begin|> +【2025-03-01】 + --:-- [直播-关注] <|living_begin|> +【2025-03-24】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [直播-关注] <|living_begin|> +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> +【2025-04-09】 + --:-- [直播-关注] <|living_begin|> +【2025-04-13】 + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [直播-关注] <|living_begin|> +【2025-04-27】 + --:-- [直播-关注] <|living_begin|> +【2025-04-30】 + --:-- [直播-关注] <|living_begin|> +【2025-05-09】 + --:-- [直播-关注] <|living_begin|> +【2025-05-25】 + --:-- [直播-关注] <|living_begin|> +【2025-05-30】 + --:-- [直播-关注] <|living_begin|> +【2025-06-29】 + --:-- [直播-关注] <|living_begin|> +【2025-07-16】 + --:-- [直播-关注] <|living_begin|> +【2025-07-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-28】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [直播-关注] <|living_begin|> +【2025-09-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-27】 + --:-- [直播-关注] <|living_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + 09:08 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 11:23 [广告-深度转化] <|ad_begin|> + 20:40 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 09:15 [广告-深度转化] <|ad_begin|> + 19:32 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 07:55 [广告-深度转化] <|ad_begin|> + 10:18 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 11:31 [广告-深度转化] <|ad_begin|> + 12:06 [广告-深度转化] <|ad_begin|> + 13:00 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + 08:12 [广告-深度转化] <|ad_begin|> + 11:25 [广告-深度转化] <|ad_begin|> + 13:13 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 08:31 [广告-深度转化] <|ad_begin|> + 09:12 [广告-深度转化] <|ad_begin|> + 09:13 [广告-深度转化] <|ad_begin|> + 09:13 [广告-深度转化] <|ad_begin|> + 11:12 [广告-深度转化] <|ad_begin|> + 11:53 [广告-深度转化] <|ad_begin|> + 13:11 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 03:10 [广告-深度转化] <|ad_begin|> + 03:28 [广告-深度转化] <|ad_begin|> + 07:51 [广告-深度转化] <|ad_begin|> + 07:52 [广告-深度转化] <|ad_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 07:54 [广告-深度转化] <|ad_begin|> + 10:14 [广告-深度转化] <|ad_begin|> + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + 22:59 [广告-点击] <|ad_begin|> + 23:00 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + 11:44 [广告-深度转化] <|ad_begin|> + 11:44 [广告-点击] <|ad_begin|> + 12:44 [广告-点击] <|ad_begin|> + 14:10 [广告-点击] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 23:46 [广告-深度转化] <|ad_begin|> + 23:46 [广告-点击] <|ad_begin|> +【2026-01-14】 + 00:22 [广告-点击] <|ad_begin|> + 05:31 [广告-点击] <|ad_begin|> +【2026-01-15】 + 07:51 [广告-深度转化] <|ad_begin|> + 07:51 [广告-点击] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> + 11:47 [广告-点击] <|ad_begin|> + 14:35 [广告-点击] <|ad_begin|> + 21:01 [广告-深度转化] <|ad_begin|> + 21:01 [广告-点击] <|ad_begin|> +【2026-01-16】 + 08:29 [广告-深度转化] <|ad_begin|> + 08:29 [广告-点击] <|ad_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 13:31 [广告-点击] <|ad_begin|> + 17:35 [广告-点击] <|ad_begin|> +【2026-01-17】 + 03:51 [广告-深度转化] <|ad_begin|> + 03:51 [广告-点击] <|ad_begin|> + 09:22 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:59 [广告-点击] <|ad_begin|> +【2026-01-18】 + 07:55 [广告-点击] <|ad_begin|> +【2026-01-19】 + 08:05 [广告-深度转化] <|ad_begin|> + 08:05 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + 08:24 [广告-深度转化] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 14:35 [广告-深度转化] <|ad_begin|> + 14:35 [广告-点击] <|ad_begin|> + 22:42 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + 03:22 [广告-点击] <|ad_begin|> + 08:29 [广告-深度转化] <|ad_begin|> + 08:29 [广告-点击] <|ad_begin|> + 10:55 [广告-深度转化] <|ad_begin|> + 10:55 [广告-点击] <|ad_begin|> + 13:01 [广告-深度转化] <|ad_begin|> + 13:01 [广告-点击] <|ad_begin|> +【2026-01-22】 + 03:41 [广告-深度转化] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + 05:37 [视频-收藏/点赞/长播] <|video_begin|> + 05:37 [视频-收藏/长播] <|video_begin|> + 07:53 [广告-点击] <|ad_begin|> + 08:23 [广告-点击] <|ad_begin|> + 10:05 [视频-关注/长播] <|video_begin|> + 14:08 [广告-深度转化] <|ad_begin|> + 14:08 [广告-点击] <|ad_begin|> + 14:09 [视频-收藏/长播] <|video_begin|> + 14:52 [广告-点击] <|ad_begin|> +【2026-01-23】 + 05:42 [视频-关注/长播] <|video_begin|> + 06:19 [视频-关注/长播] <|video_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 21:24 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + 05:53 [视频-收藏/点赞/长播] <|video_begin|> + 07:34 [广告-点击] <|ad_begin|> + 08:00 [广告-深度转化] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> + 15:03 [广告-深度转化] <|ad_begin|> + 15:03 [广告-点击] <|ad_begin|> + 23:11 [广告-点击] <|ad_begin|> + 23:11 [视频-浏览] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:24 [视频-收藏/长播] <|video_begin|> + 04:24 [视频-关注/长播] <|video_begin|> + 14:11 [广告-深度转化] <|ad_begin|> + 14:11 [广告-点击] <|ad_begin|> + 22:40 [广告-点击] <|ad_begin|> + 23:05 [广告-点击] <|ad_begin|> + 23:18 [视频-浏览] <|video_begin|> + 23:47 [视频-点赞/长播] <|video_begin|> + 23:57 [视频-点赞/长播] <|video_begin|> +【2026-01-26】 + 03:44 [广告-点击] <|ad_begin|> + 07:29 [广告-深度转化] <|ad_begin|> + 07:29 [广告-点击] <|ad_begin|> + 22:18 [视频-收藏/长播] <|video_begin|> + 23:18 [视频-收藏/长播] <|video_begin|> + 23:36 [视频-收藏/长播] <|video_begin|> +【2026-01-27】 + 15:21 [广告-深度转化] <|ad_begin|> + 15:21 [广告-点击] <|ad_begin|> + 23:13 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 05:19 [视频-点赞/长播] <|video_begin|> + 05:48 [视频-点赞/长播] <|video_begin|> + 09:02 [广告-深度转化] <|ad_begin|> + 09:02 [广告-点击] <|ad_begin|> + 11:30 [广告-深度转化] <|ad_begin|> + 11:30 [广告-点击] <|ad_begin|> + 19:31 [视频-收藏/长播] <|video_begin|> + 19:51 [广告-深度转化] <|ad_begin|> + 19:51 [广告-点击] <|ad_begin|> + 20:30 [视频-点赞/长播] <|video_begin|> + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> + 20:40 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + 05:45 [广告-点击] <|ad_begin|> + 11:59 [视频-浏览] <|video_begin|> + 13:02 [广告-深度转化] <|ad_begin|> + 13:02 [广告-点击] <|ad_begin|> + 14:43 [视频-点赞/长播] <|video_begin|> + 19:05 [视频-点赞/长播] <|video_begin|> + 19:50 [视频-转发/长播] <|video_begin|> +【2026-01-30】 + 06:13 [视频-收藏/长播] <|video_begin|> + 12:37 [广告-深度转化] <|ad_begin|> + 12:37 [广告-点击] <|ad_begin|> + 20:56 [视频-收藏/长播] <|video_begin|> +【2026-01-31】 + 08:36 [广告-深度转化] <|ad_begin|> + 08:36 [广告-点击] <|ad_begin|> + 11:30 [广告-深度转化] <|ad_begin|> + 11:30 [广告-点击] <|ad_begin|> + 15:06 [视频-收藏/长播] <|video_begin|> + 23:18 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 06:32 [视频-转发/长播] <|video_begin|> + 07:42 [广告-点击] <|ad_begin|> + 07:47 [视频-关注/收藏/长播] <|video_begin|> + 11:41 [广告-深度转化] <|ad_begin|> + 11:41 [广告-点击] <|ad_begin|> + 11:46 [视频-收藏/点赞/长播] <|video_begin|> + 14:57 [视频-点赞/长播] <|video_begin|> + 22:03 [视频-收藏/长播] <|video_begin|> + 22:08 [视频-收藏/点赞/长播] <|video_begin|> + 23:02 [视频-收藏/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 08:33 [广告-点击] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 08:59 [广告-点击] <|ad_begin|> + 11:23 [视频-转发/长播] <|video_begin|> + 13:28 [视频-转发/长播] <|video_begin|> + 14:06 [视频-转发/长播] <|video_begin|> + 23:04 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-03】 + 08:13 [广告-深度转化] <|ad_begin|> + 08:13 [广告-点击] <|ad_begin|> + 11:16 [广告-深度转化] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:36 [广告-深度转化] <|ad_begin|> + 11:36 [广告-点击] <|ad_begin|> + 12:16 [视频-关注/转发/收藏/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|> +【2026-02-04】 + 05:08 [视频-关注/长播] <|video_begin|> + 07:49 [广告-深度转化] <|ad_begin|> + 07:49 [广告-点击] <|ad_begin|> + 12:04 [视频-收藏/点赞/长播] <|video_begin|> + 12:13 [视频-浏览] <|video_begin|> + 14:30 [视频-点赞/长播] <|video_begin|> + 21:20 [视频-长播] <|video_begin|> + 21:22 [视频-长播] <|video_begin|> + 21:22 [视频-长播] <|video_begin|> + 21:22 [视频-长播] <|video_begin|> + 21:23 [视频-长播] <|video_begin|> + 21:23 [视频-长播] <|video_begin|> + 21:27 [视频-长播] <|video_begin|> + 21:27 [视频-长播] <|video_begin|> + 21:29 [视频-长播] <|video_begin|> + 21:30 [视频-长播] <|video_begin|> + 21:38 [视频-长播] <|video_begin|> + 21:44 [视频-长播] <|video_begin|> + 21:44 [视频-长播] <|video_begin|> + 21:58 [视频-长播] <|video_begin|> + 21:58 [视频-长播] <|video_begin|> + 22:01 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [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|> +【2026-02-05】 + 02:57 [视频-长播] <|video_begin|> + 02:57 [视频-长播] <|video_begin|> + 03:07 [视频-长播] <|video_begin|> + 03:07 [视频-长播] <|video_begin|> + 03:20 [视频-长播] <|video_begin|> + 03:20 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 06:05 [视频-长播] <|video_begin|> + 06:05 [视频-点赞/长播] <|video_begin|> + 06:16 [视频-长播] <|video_begin|> + 06:16 [视频-长播] <|video_begin|> + 07:28 [视频-长播] <|video_begin|> + 08:12 [广告-深度转化] <|ad_begin|> + 08:12 [广告-点击] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 08:54 [视频-长播] <|video_begin|> + 10:27 [视频-长播] <|video_begin|> + 10:27 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:42 [视频-长播] <|video_begin|> + 11:44 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 14:12 [视频-长播] <|video_begin|> + 14:14 [广告-点击] <|ad_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:47 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +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）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心提取逻辑（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 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下，交互 B 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请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健康方案的探索与决策 + +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健康方案的探索与决策", + "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" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化健康焦虑到具体健康方案的探索与决策", "events": [{"date": "2025-09-05", "action": "[直播-关注] <|living_begin|>", "logic": "问题触发与信息输入：开始关注中医科普，兴趣点集中在“脾胃病调理”、“大便辨证”等相对宽泛的身体调理领域，表现为被动知识接收。"}, {"date": "2025-10-02", "action": "[商品-购买] <|prod_begin|>", "logic": "首次聚焦与尝试：从宽泛的中医调理转向购买具有明确成分（精氨酸）和功能指向（男性精力）的西式补剂，实现了从理论吸收到具体产品决策的认知跃迁。"}, {"date": "2025-10-09", "action": "[商品-购买] <|prod_begin|>", "logic": "问题具体化与方案收敛：兴趣点进一步收窄，从提升“精力”的模糊概念，聚焦到解决“腰疼腿疼”、“手脚发麻”等具体痛症，购买了针对性更强的药品，完成了从补充剂到治疗药物的认知深化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-08-07】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-09】 + --:-- [商品-购买] <|prod_begin|> +【2023-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2024-01-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-05】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + 14:32 [广告-深度转化] <|ad_begin|> +【2025-12-12】 + 09:55 [广告-深度转化] <|ad_begin|> + 10:22 [广告-深度转化] <|ad_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + 11:04 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + 00:12 [广告-深度转化] <|ad_begin|> + 05:52 [广告-深度转化] <|ad_begin|> + 12:42 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 00:45 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 00:13 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 01:15 [广告-深度转化] <|ad_begin|> + 12:53 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:07 [广告-深度转化] <|ad_begin|> + 00:49 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + 01:13 [广告-深度转化] <|ad_begin|> + 06:06 [广告-深度转化] <|ad_begin|> + 07:58 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> + 13:21 [广告-深度转化] <|ad_begin|> + 23:47 [广告-深度转化] <|ad_begin|> + 23:47 [广告-点击] <|ad_begin|> +【2025-12-24】 + 13:46 [广告-点击] <|ad_begin|> + 23:35 [广告-深度转化] <|ad_begin|> + 23:35 [广告-点击] <|ad_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> + 12:05 [广告-深度转化] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> +【2025-12-26】 + 00:07 [广告-深度转化] <|ad_begin|> + 00:07 [广告-点击] <|ad_begin|> +【2025-12-27】 + 00:10 [广告-深度转化] <|ad_begin|> + 00:10 [广告-点击] <|ad_begin|> + 00:47 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:55 [广告-深度转化] <|ad_begin|> + 23:55 [广告-点击] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> + 13:30 [广告-点击] <|ad_begin|> + 13:34 [广告-深度转化] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> +【2025-12-29】 + 00:02 [广告-深度转化] <|ad_begin|> + 00:02 [广告-点击] <|ad_begin|> + 02:45 [广告-深度转化] <|ad_begin|> + 02:45 [广告-点击] <|ad_begin|> + 06:19 [广告-深度转化] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 07:26 [广告-深度转化] <|ad_begin|> + 07:26 [广告-点击] <|ad_begin|> + 13:13 [广告-深度转化] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 23:31 [广告-深度转化] <|ad_begin|> + 23:31 [广告-点击] <|ad_begin|> +【2025-12-30】 + 02:29 [广告-点击] <|ad_begin|> + 10:49 [广告-深度转化] <|ad_begin|> + 10:49 [广告-点击] <|ad_begin|> + 13:30 [广告-深度转化] <|ad_begin|> + 13:30 [广告-点击] <|ad_begin|> +【2025-12-31】 + 01:10 [广告-深度转化] <|ad_begin|> + 01:10 [广告-点击] <|ad_begin|> + 01:44 [广告-点击] <|ad_begin|> + 02:03 [广告-点击] <|ad_begin|> + 02:06 [广告-点击] <|ad_begin|> +【2026-01-03】 + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + 13:43 [广告-深度转化] <|ad_begin|> + 13:43 [广告-点击] <|ad_begin|> +【2026-01-04】 + 00:07 [广告-深度转化] <|ad_begin|> + 00:07 [广告-点击] <|ad_begin|> + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:11 [广告-点击] <|ad_begin|> + 10:19 [广告-深度转化] <|ad_begin|> + 10:19 [广告-点击] <|ad_begin|> +【2026-01-05】 + 01:36 [广告-点击] <|ad_begin|> + 01:51 [广告-深度转化] <|ad_begin|> + 01:51 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> +【2026-01-06】 + 00:24 [广告-深度转化] <|ad_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 00:06 [广告-深度转化] <|ad_begin|> + 00:06 [广告-点击] <|ad_begin|> + 04:55 [广告-深度转化] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 13:43 [广告-深度转化] <|ad_begin|> + 13:43 [广告-点击] <|ad_begin|> + 22:24 [广告-点击] <|ad_begin|> + 22:31 [广告-深度转化] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> +【2026-01-10】 + 00:23 [广告-点击] <|ad_begin|> + 00:44 [广告-深度转化] <|ad_begin|> + 00:44 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + 00:04 [广告-点击] <|ad_begin|> + 00:12 [广告-深度转化] <|ad_begin|> + 00:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:53 [广告-深度转化] <|ad_begin|> + 03:53 [广告-点击] <|ad_begin|> +【2026-01-14】 + 02:17 [广告-深度转化] <|ad_begin|> + 02:17 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> + 23:34 [广告-深度转化] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-17】 + 00:04 [广告-深度转化] <|ad_begin|> + 00:04 [广告-点击] <|ad_begin|> + 00:32 [广告-深度转化] <|ad_begin|> + 00:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:20 [广告-深度转化] <|ad_begin|> + 07:20 [广告-点击] <|ad_begin|> +【2026-01-18】 + 23:26 [广告-深度转化] <|ad_begin|> + 23:26 [广告-点击] <|ad_begin|> +【2026-01-19】 + 02:51 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 01:55 [广告-点击] <|ad_begin|> + 02:52 [广告-深度转化] <|ad_begin|> + 02:52 [广告-点击] <|ad_begin|> + 06:31 [广告-点击] <|ad_begin|> + 06:32 [视频-长播] <|video_begin|> + 06:34 [视频-点赞/长播] <|video_begin|> + 07:48 [广告-深度转化] <|ad_begin|> + 07:48 [广告-点击] <|ad_begin|> + 08:15 [视频-长播] <|video_begin|> + 08:15 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:40 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 09:24 [视频-长播] <|video_begin|> + 09:24 [视频-长播] <|video_begin|> +【2026-01-23】 + 00:44 [广告-深度转化] <|ad_begin|> + 00:44 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:43 [视频-长播] <|video_begin|> +【2026-01-24】 + 01:05 [视频-长播] <|video_begin|> + 01:22 [广告-深度转化] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:12 [视频-长播] <|video_begin|> +【2026-01-25】 + 06:04 [视频-长播] <|video_begin|> + 06:04 [视频-长播] <|video_begin|> + 12:56 [广告-深度转化] <|ad_begin|> + 12:56 [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-购买] <|prod_begin|> + 05:13 [视频-长播] <|video_begin|> + 05:13 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 05:36 [视频-长播] <|video_begin|> + 06:05 [广告-深度转化] <|ad_begin|> + 06:05 [广告-点击] <|ad_begin|> + 10:18 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:45 [广告-深度转化] <|ad_begin|> + 10:45 [广告-点击] <|ad_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 23:55 [广告-深度转化] <|ad_begin|> + 23:55 [广告-点击] <|ad_begin|> +【2026-01-27】 + 02:23 [视频-长播] <|video_begin|> + 02:23 [视频-长播] <|video_begin|> + 02:36 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:41 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> +【2026-01-28】 + 12:32 [视频-长播] <|video_begin|> + 13:48 [视频-长播] <|video_begin|> + 13:48 [视频-长播] <|video_begin|> + 13:48 [视频-长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> +【2026-01-29】 + 01:11 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + 22:52 [广告-深度转化] <|ad_begin|> + 22:52 [广告-点击] <|ad_begin|> +【2026-01-31】 + 14:35 [广告-深度转化] <|ad_begin|> + 14:35 [广告-点击] <|ad_begin|> +【2026-02-01】 + 11:37 [视频-长播] <|video_begin|> + 13:01 [视频-长播] <|video_begin|> +【2026-02-02】 + 09:22 [广告-深度转化] <|ad_begin|> + 09:22 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:00 [视频-长播] <|video_begin|> + 03:11 [视频-长播] <|video_begin|> + 03:29 [广告-深度转化] <|ad_begin|> + 03:29 [广告-点击] <|ad_begin|> + 10:10 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> +【2026-02-04】 + 07:08 [视频-长播] <|video_begin|> + 07:14 [视频-长播] <|video_begin|> + 07:27 [广告-点击] <|ad_begin|> + 15:17 [视频-长播] <|video_begin|> + 23:08 [视频-长播] <|video_begin|> + 23:08 [视频-浏览] <|video_begin|> + 23:14 [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）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +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 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在相同主题类目下，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。 +请针对下列主题提取行为逻辑链： +主题：从被动接收信息到主动解决问题的行为闭环 + +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解决问题的行为闭环", + "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，我将以推荐分析师的视角，对这项任务进行推理。 + +我接到的任务是分析用户的行为，并围绕“从被动接收信息到主动解决问题的行为闭环”这一主题，构建一条具有内在逻辑的行为链。这意味着我需要从用户的交互历史中，找到一个从被动获取知识，到主动确认并可能付诸行动的完整过程。 + +在梳理用户的整体行为时间线时，我注意到用户的消费偏好非常广泛，既有家居日用品、食品饮料，也有大量的保健品和一些带有玄学色彩的转运、开运商品。这表明用户可能是一位关注生活品质和身心健康的中老年人。此外，用户的行为在2025年12月后发生了显著变化，开始大量观看和点击短剧广告，显示出对剧情类内容的强烈兴趣。 + +然而，我的核心任务是寻找一个从“被动接收”到“主动解决”的闭环。在浏览到2026年1月22日的记录时，我发现了一个非常清晰的信号。当天，用户观看了一个关于养牛技巧的视频 <|video_begin|>。这个视频内容非常具体，提到了牛圈管理和一种名为“全驱净”的驱虫方法。结合用户在2024年初购买过多种饲料添加剂，例如“牧饲康酿酒酵母开胃王” <|prod_begin|> 和“牛羊全驱净” <|prod_begin|>，以及在2025年购买过测量猪牛体重的尺子 <|prod_begin|>，我推断用户很可能从事或关心畜牧养殖，特别是牛羊养殖。 + +因此，观看这个养牛技巧视频 <|video_begin|> 的行为，可以被视为一个关键的“信息获取”环节。用户可能是在浏览时偶然看到了这个视频，也可能是主动搜索相关内容，但无论如何，这个行为代表了他正在被动或主动地接收关于养牛的专业知识。视频中明确提到的“全驱净驱虫方法”，直接为用户提供了一个具体的问题解决方案。 + +紧接着，我注意到一个关键的后续动作：用户不仅观看了这个视频，还对它进行了点赞。这个“点赞”行为虽然看似微小，但意义重大。它不再是被动地观看，而是一种主动的、明确的价值认同和正向反馈。这表明视频中提到的养牛技巧，特别是驱虫方法，精准地命中了用户的需求点，或者解决了他的某个困惑。通过点赞，用户完成了从“看到信息”到“认可信息”的认知闭环。这个行为链条完美地诠释了从被动接收知识到主动确认其价值的过程，形成了一个完整的行为闭环。 + +综合以上分析，我认为用户的行为逻辑链可以这样归纳：首先，用户通过观看一个关于养牛技巧的视频 <|video_begin|>，获取了关于“全驱净驱虫方法”的具体信息，这构成了问题触发与信息获取的起点。随后，用户对该视频进行了点赞 <|video_begin|>，这一主动行为表明了他对视频内容的认可，完成了从信息吸收到价值认同的认知闭环。 +{"logic_chain": {"name": "从被动接收信息到主动解决问题的行为闭环", "events": [{"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "问题触发与信息获取：用户被动或主动观看了关于“养牛技巧”的科普视频，其中明确提及了“全驱净驱虫方法”，为后续行为提供了具体的知识输入和问题解决方案。"}, {"date": "2026-01-22", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "认知确认与行为闭环：用户对视频内容（特别是驱虫方法）表示认可，通过“点赞”这一主动行为表达了正向反馈，并完成了从信息吸收到价值认同的闭环。"}]}}<|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" + } + ] + } +}/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】： +【2024-05-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-07-09】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-15】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-31】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-11-09】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-08】 + --:-- [直播-关注] <|living_begin|> +【2025-03-07】 + --:-- [直播-关注] <|living_begin|> +【2025-03-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> +【2025-04-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-05-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-30】 + --:-- [直播-关注] <|living_begin|> +【2025-06-02】 + --:-- [直播-关注] <|living_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-27】 + --:-- [直播-关注] <|living_begin|> +【2025-07-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-06】 + --:-- [直播-关注] <|living_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-24】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-24】 + --:-- [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|> +【2025-12-04】 + --:-- [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|> +【2025-12-05】 + --:-- [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|> +【2025-12-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-13】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-15】 + --:-- [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|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:53 [广告-点击] <|ad_begin|> +【2025-12-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + 12:50 [广告-点击] <|ad_begin|> +【2025-12-26】 + 12:53 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-28】 + 05:19 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-04】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 12:01 [视频-评论/长播] <|video_begin|> + 12:52 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-23】 + 11:13 [视频-评论/点赞/长播] <|video_begin|> + 11:18 [视频-评论/点赞/长播] <|video_begin|> + 11:22 [视频-评论/长播] <|video_begin|> + 11:56 [视频-浏览] <|video_begin|> + 15:01 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-24】 + 09:15 [视频-评论/长播] <|video_begin|> +【2026-01-25】 + 05:32 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 16:34 [视频-评论/长播] <|video_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + 06:00 [视频-评论/点赞/长播] <|video_begin|> + 13:24 [视频-评论/点赞/长播] <|video_begin|> + 14:29 [视频-评论/长播] <|video_begin|> + 14:32 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-27】 + 00:55 [视频-评论/长播] <|video_begin|> + 01:03 [视频-评论/长播] <|video_begin|> + 07:16 [视频-评论/长播] <|video_begin|> + 07:29 [视频-评论/长播] <|video_begin|> + 07:29 [视频-评论/长播] <|video_begin|> + 08:03 [视频-关注/转发/评论/点赞/长播] <|video_begin|> + 13:53 [视频-评论/长播] <|video_begin|> + 13:53 [视频-评论/长播] <|video_begin|> + 13:57 [视频-转发/点赞/长播] <|video_begin|> + 14:18 [视频-评论/收藏/长播] <|video_begin|> + 14:32 [广告-点击] <|ad_begin|> + 15:06 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-28】 + 03:32 [视频-转发/点赞/长播] <|video_begin|> + 03:39 [视频-评论/点赞/长播] <|video_begin|> + 05:57 [视频-评论/长播] <|video_begin|> + 09:12 [视频-评论/长播] <|video_begin|> + 15:51 [视频-评论/长播] <|video_begin|> + 16:01 [视频-评论/长播] <|video_begin|> + 16:29 [视频-收藏/长播] <|video_begin|> + 16:48 [视频-关注/点赞/长播] <|video_begin|> + 16:55 [视频-浏览] <|video_begin|> + 16:59 [视频-评论/长播] <|video_begin|> + 16:59 [视频-评论/点赞/长播] <|video_begin|> + 16:59 [视频-关注/点赞/长播] <|video_begin|> + 17:05 [视频-关注/点赞/长播] <|video_begin|> + 17:21 [视频-关注/点赞/长播] <|video_begin|> +【2026-01-29】 + 02:19 [视频-转发/长播] <|video_begin|> + 02:28 [视频-评论/点赞/长播] <|video_begin|> + 02:31 [视频-关注/点赞/长播] <|video_begin|> + 12:13 [视频-评论/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 15:06 [视频-评论/点赞/长播] <|video_begin|> + 15:55 [视频-评论/长播] <|video_begin|> + 17:03 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-31】 + 15:52 [视频-评论/长播] <|video_begin|> + 16:08 [视频-评论/长播] <|video_begin|> + 16:17 [视频-评论/长播] <|video_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:26 [视频-评论/长播] <|video_begin|> + 17:06 [广告-点击] <|ad_begin|> +【2026-02-02】 + 05:56 [视频-转发/点赞/长播] <|video_begin|> + 07:13 [视频-转发/点赞/长播] <|video_begin|> + 09:18 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:54 [视频-评论/点赞/长播] <|video_begin|> + 14:54 [视频-评论/点赞/长播] <|video_begin|> + 14:54 [视频-收藏/长播] <|video_begin|> +【2026-02-03】 + 00:53 [视频-点赞/长播] <|video_begin|> + 00:53 [视频-点赞/长播] <|video_begin|> + 00:57 [视频-点赞/长播] <|video_begin|> + 00:57 [视频-点赞/长播] <|video_begin|> + 00:57 [视频-点赞/长播] <|video_begin|> + 00:57 [视频-点赞/长播] <|video_begin|> + 01:04 [视频-点赞/长播] <|video_begin|> + 03:03 [视频-点赞/长播] <|video_begin|> + 03:03 [视频-点赞/长播] <|video_begin|> + 06:02 [视频-转发/点赞/长播] <|video_begin|> + 06:14 [视频-点赞/长播] <|video_begin|> + 06:18 [视频-点赞/长播] <|video_begin|> + 06:24 [视频-转发/点赞/长播] <|video_begin|> + 06:27 [视频-转发/点赞/长播] <|video_begin|> + 06:27 [视频-关注/点赞/长播] <|video_begin|> + 06:34 [视频-点赞/长播] <|video_begin|> + 06:36 [视频-浏览] <|video_begin|> + 06:37 [视频-点赞/长播] <|video_begin|> + 06:40 [视频-评论/点赞/长播] <|video_begin|> + 06:44 [视频-转发/长播] <|video_begin|> + 06:50 [视频-点赞/长播] <|video_begin|> + 15:53 [视频-点赞/长播] <|video_begin|> + 16:32 [视频-点赞/长播] <|video_begin|> + 16:37 [视频-点赞/长播] <|video_begin|> + 16:39 [视频-点赞/长播] <|video_begin|> + 16:39 [视频-点赞/长播] <|video_begin|> + 16:40 [视频-点赞/长播] <|video_begin|> + 16:40 [视频-点赞/长播] <|video_begin|> + 16:49 [视频-点赞/长播] <|video_begin|> + 16:49 [视频-长播] <|video_begin|> +【2026-02-04】 + 03:46 [视频-点赞/长播] <|video_begin|> + 03:46 [视频-长播] <|video_begin|> + 03:53 [视频-长播] <|video_begin|> + 03:55 [视频-点赞/长播] <|video_begin|> + 03:55 [视频-点赞/长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 04:01 [视频-点赞/长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:03 [视频-点赞/长播] <|video_begin|> + 04:03 [视频-点赞/长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:06 [视频-点赞/长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:08 [视频-点赞/长播] <|video_begin|> + 04:12 [视频-点赞/长播] <|video_begin|> + 04:12 [视频-点赞/长播] <|video_begin|> + 04:12 [视频-长播] <|video_begin|> + 04:14 [视频-点赞/长播] <|video_begin|> + 04:18 [视频-长播] <|video_begin|> + 04:18 [视频-浏览] <|video_begin|> + 04:22 [视频-长播] <|video_begin|> + 04:23 [视频-点赞/长播] <|video_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:26 [视频-长播] <|video_begin|> + 04:28 [视频-点赞/长播] <|video_begin|> + 04:30 [视频-长播] <|video_begin|> + 07:50 [视频-长播] <|video_begin|> + 07:50 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:05 [视频-长播] <|video_begin|> + 08:05 [视频-长播] <|video_begin|> + 08:05 [视频-长播] <|video_begin|> + 08:07 [视频-长播] <|video_begin|> + 08:14 [视频-长播] <|video_begin|> + 08:14 [视频-长播] <|video_begin|> + 08:15 [视频-点赞/长播] <|video_begin|> + 08:16 [视频-长播] <|video_begin|> + 08:16 [视频-长播] <|video_begin|> + 08:37 [视频-长播] <|video_begin|> + 08:37 [视频-长播] <|video_begin|> + 08:38 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 08:52 [视频-长播] <|video_begin|> + 08:56 [视频-长播] <|video_begin|> + 08:56 [视频-长播] <|video_begin|> + 08:56 [视频-长播] <|video_begin|> + 08:59 [视频-点赞/长播] <|video_begin|> + 08:59 [视频-点赞/长播] <|video_begin|> + 08:59 [视频-长播] <|video_begin|> + 09:54 [视频-点赞/长播] <|video_begin|> + 09:58 [视频-点赞/长播] <|video_begin|> + 09:58 [视频-点赞/长播] <|video_begin|> + 09:58 [视频-长播] <|video_begin|> + 18:42 [视频-点赞/长播] <|video_begin|> + 18:42 [视频-点赞/长播] <|video_begin|> + 18:42 [视频-长播] <|video_begin|> + 18:42 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:47 [视频-长播] <|video_begin|> + 00:47 [视频-长播] <|video_begin|> + 00:47 [视频-长播] <|video_begin|> + 00:51 [广告-点击] <|ad_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:58 [视频-点赞/长播] <|video_begin|> + 00:58 [视频-长播] <|video_begin|> + 04:48 [视频-长播] <|video_begin|> + 05:20 [视频-长播] <|video_begin|> + 05:42 [视频-点赞/长播] <|video_begin|> + 05:45 [视频-点赞/长播] <|video_begin|> + 05:45 [视频-转发/点赞/长播] <|video_begin|> + 05:47 [视频-点赞/长播] <|video_begin|> + 05:47 [视频-点赞/长播] <|video_begin|> + 05:47 [视频-长播] <|video_begin|> + 05:49 [视频-点赞/长播] <|video_begin|> + 05:50 [视频-点赞/长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题提炼具有递进逻辑与内部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 +核心提取逻辑（递进性准则）： +按时间顺序提取 5 步以内的行为链，要求后项交互B必须是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是因为 A 才产生的延伸需求，严禁提取任何品类平级、内容重复或仅因天气/促销产生的凑单并列行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 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。 +请面向以下主题提取行为逻辑链： +主题：从被动接收到主动决策的手机升级路径 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收到主动决策的手机升级路径", + "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" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从被动接收到主动决策的手机升级路径", "events": [{"date": "2025-12-26", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收到关于荣耀新机长续航和快充特性的广告信息，产生初步兴趣。"}, {"date": "2025-12-27", "action": "[商品-点击] <|prod_begin|>", "logic": "行为转化：从广告曝光转化为主动点击广告中提及的同系列商品，验证广告信息的真实性。"}, {"date": "2026-01-25", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知深化与对比：兴趣从单一品牌扩展到跨品牌的高性能设备对比，开始关注核心硬件参数（如骁龙8 Gen 3芯片、120Hz刷新率），表明用户正在建立自己的评判标准。"}, {"date": "2026-02-01", "action": "[商品-点击] <|prod_begin|>", "logic": "决策收敛：在经过跨品牌性能对比后，用户的兴趣最终聚焦到另一款以高性能和游戏体验著称的iQOO品牌旗舰新品上，并产生了预约点击行为，完成了从被动了解到主动决策的闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-03-09】 + --:-- [直播-关注] <|living_begin|> +【2025-06-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-25】 + --:-- [直播-关注] <|living_begin|> +【2025-06-30】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-02】 + --:-- [直播-关注] <|living_begin|> +【2025-07-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-13】 + --:-- [直播-关注] <|living_begin|> +【2025-07-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-22】 + --:-- [直播-关注] <|living_begin|> +【2025-08-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-08-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-09】 + --:-- [直播-关注] <|living_begin|> +【2025-09-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-23】 + --:-- [直播-关注] <|living_begin|> +【2025-09-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-12】 + --:-- [直播-关注] <|living_begin|> +【2025-10-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [直播-关注] <|living_begin|> +【2025-11-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-03】 + --:-- [直播-关注] <|living_begin|> +【2025-11-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [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|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> +【2025-12-12】 + --:-- [直播-关注] <|living_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + 04:48 [广告-点击] <|ad_begin|> +【2025-12-18】 + 06:36 [广告-深度转化] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> +【2025-12-27】 + 04:11 [广告-点击] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-07】 + 08:02 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-12】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 09:06 [视频-长播] <|video_begin|> + 09:06 [视频-长播] <|video_begin|> + 09:06 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-23】 + 11:01 [视频-长播] <|video_begin|> + 11:01 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> +【2026-01-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + 02:27 [视频-长播] <|video_begin|> + 16:43 [视频-长播] <|video_begin|> + 16:47 [视频-长播] <|video_begin|> + 16:47 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-04】 + 23:23 [视频-长播] <|video_begin|> +【2026-02-05】 + 13:05 [视频-长播] <|video_begin|> +角色任务：你需要扮演极端严苛的用户行为数据挖掘专家以及数据格式化专家，基于以上用户交互历史，针对给定主题识别并提取具有递进逻辑和内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 与交互 A 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请面向以下主题提取行为逻辑链： +主题：从被动健康信息接收到主动解决特定隐疾 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动健康信息接收到主动解决特定隐疾", + "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" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从被动健康信息接收到主动解决特定隐疾", "events": [{"date": "2026-01-07", "action": "[广告-点击] <|ad_begin|>", "logic": "问题触发：被动接收到一则宽泛的男性健康管理广告，唤醒了潜在的健康需求意识。"}, {"date": "2026-01-22", "action": "[商品-购买] <|prod_begin|>", "logic": "行为收敛与解决：在被唤醒健康意识后，用户迅速将宽泛的需求聚焦到一个非常具体的、带有隐私性的问题（肛肠不适），并直接购买了针对性极强的外用产品，完成了从“意识”到“行动”的转化。"}]}}<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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"问题具体化与方案收敛：兴趣点进一步收窄，从提升“精力”的模糊概念，聚焦到解决“腰疼腿疼”、“手脚发麻”等具体痛症，购买了针对性更强的药品，完成了从补充剂到治疗药物的认知深化。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这项任务进行推理。 + +我接到的任务是分析用户的行为，并围绕“从被动接收信息到主动解决问题的行为闭环”这一主题，构建一条具有内在逻辑的行为链。这意味着我需要从用户的交互历史中，找到一个从被动获取知识，到主动确认并可能付诸行动的完整过程。 + +在梳理用户的整体行为时间线时，我注意到用户的消费偏好非常广泛，既有家居日用品、食品饮料，也有大量的保健品和一些带有玄学色彩的转运、开运商品。这表明用户可能是一位关注生活品质和身心健康的中老年人。此外，用户的行为在2025年12月后发生了显著变化，开始大量观看和点击短剧广告，显示出对剧情类内容的强烈兴趣。 + +然而，我的核心任务是寻找一个从“被动接收”到“主动解决”的闭环。在浏览到2026年1月22日的记录时，我发现了一个非常清晰的信号。当天，用户观看了一个关于养牛技巧的视频 <|video_begin|>。这个视频内容非常具体，提到了牛圈管理和一种名为“全驱净”的驱虫方法。结合用户在2024年初购买过多种饲料添加剂，例如“牧饲康酿酒酵母开胃王” <|prod_begin|> 和“牛羊全驱净” <|prod_begin|>，以及在2025年购买过测量猪牛体重的尺子 <|prod_begin|>，我推断用户很可能从事或关心畜牧养殖，特别是牛羊养殖。 + +因此，观看这个养牛技巧视频 <|video_begin|> 的行为，可以被视为一个关键的“信息获取”环节。用户可能是在浏览时偶然看到了这个视频，也可能是主动搜索相关内容，但无论如何，这个行为代表了他正在被动或主动地接收关于养牛的专业知识。视频中明确提到的“全驱净驱虫方法”，直接为用户提供了一个具体的问题解决方案。 + +紧接着，我注意到一个关键的后续动作：用户不仅观看了这个视频，还对它进行了点赞。这个“点赞”行为虽然看似微小，但意义重大。它不再是被动地观看，而是一种主动的、明确的价值认同和正向反馈。这表明视频中提到的养牛技巧，特别是驱虫方法，精准地命中了用户的需求点，或者解决了他的某个困惑。通过点赞，用户完成了从“看到信息”到“认可信息”的认知闭环。这个行为链条完美地诠释了从被动接收知识到主动确认其价值的过程，形成了一个完整的行为闭环。 + +综合以上分析，我认为用户的行为逻辑链可以这样归纳：首先，用户通过观看一个关于养牛技巧的视频 <|video_begin|>，获取了关于“全驱净驱虫方法”的具体信息，这构成了问题触发与信息获取的起点。随后，用户对该视频进行了点赞 <|video_begin|>，这一主动行为表明了他对视频内容的认可，完成了从信息吸收到价值认同的认知闭环。 +{"logic_chain": {"name": "从被动接收信息到主动解决问题的行为闭环", "events": [{"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "问题触发与信息获取：用户被动或主动观看了关于“养牛技巧”的科普视频，其中明确提及了“全驱净驱虫方法”，为后续行为提供了具体的知识输入和问题解决方案。"}, {"date": "2026-01-22", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "认知确认与行为闭环：用户对视频内容（特别是驱虫方法）表示认可，通过“点赞”这一主动行为表达了正向反馈，并完成了从信息吸收到价值认同的闭环。"}]}}<|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": "从被动接收到主动决策的手机升级路径", "events": [{"date": "2025-12-26", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收到关于荣耀新机长续航和快充特性的广告信息，产生初步兴趣。"}, {"date": "2025-12-27", "action": "[商品-点击] <|prod_begin|>", "logic": "行为转化：从广告曝光转化为主动点击广告中提及的同系列商品，验证广告信息的真实性。"}, {"date": "2026-01-25", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知深化与对比：兴趣从单一品牌扩展到跨品牌的高性能设备对比，开始关注核心硬件参数（如骁龙8 Gen 3芯片、120Hz刷新率），表明用户正在建立自己的评判标准。"}, {"date": "2026-02-01", "action": "[商品-点击] <|prod_begin|>", "logic": "决策收敛：在经过跨品牌性能对比后，用户的兴趣最终聚焦到另一款以高性能和游戏体验著称的iQOO品牌旗舰新品上，并产生了预约点击行为，完成了从被动了解到主动决策的闭环。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从被动健康信息接收到主动解决特定隐疾", "events": [{"date": "2026-01-07", "action": "[广告-点击] <|ad_begin|>", "logic": "问题触发：被动接收到一则宽泛的男性健康管理广告，唤醒了潜在的健康需求意识。"}, {"date": "2026-01-22", "action": "[商品-购买] <|prod_begin|>", "logic": "行为收敛与解决：在被唤醒健康意识后，用户迅速将宽泛的需求聚焦到一个非常具体的、带有隐私性的问题（肛肠不适），并直接购买了针对性极强的外用产品，完成了从“意识”到“行动”的转化。"}]}}<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-05 17:55:04,123 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-05 17:55:04,126 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "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": null, + "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": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +[INFO|2026-07-05 17:55:04] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-05 17:55:04] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-05 17:58:44,797 >> loading weights file model.safetensors from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-05 17:58:44,797 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-05 17:58:44,862 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file generation_config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-05 17:58:45,817 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "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] 2026-07-05 17:58:46,098 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-05 17:58:46] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-05 17:58:46] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-05 17:58:46] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-05 17:58:46] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-05 17:58:46] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-05 17:58:46,185 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}. +[INFO|trainer.py:1469] 2026-07-05 17:58:46,299 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-05 17:58:46,299 >> Num examples = 1,597 +[INFO|trainer.py:1471] 2026-07-05 17:58:46,299 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-05 17:58:46,299 >> Num update steps per epoch = 400 +[INFO|trainer.py:1473] 2026-07-05 17:58:46,299 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-05 17:58:46,299 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-05 17:58:46,299 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-05 17:58:46,299 >> Total optimization steps = 400 +[INFO|trainer.py:1479] 2026-07-05 17:58:46,299 >> Number of trainable parameters = 801,433,600 + 0%| | 0/400 [00:00> `use_return_dict` is deprecated! 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397/400 [53:50<00:23, 7.85s/it] 100%|█████████▉| 398/400 [53:59<00:16, 8.04s/it] 100%|█████████▉| 399/400 [54:08<00:08, 8.36s/it] 100%|██████████| 400/400 [54:10<00:00, 6.45s/it] 100%|██████████| 400/400 [54:10<00:00, 6.45s/it][INFO|trainer.py:3815] 2026-07-05 18:52:56,536 >> Saving model checkpoint to demo/output/onereason_0.8b_sft/checkpoint-400 +[INFO|configuration_utils.py:533] 2026-07-05 18:52:56,537 >> Configuration saved in demo/output/onereason_0.8b_sft/checkpoint-400/config.json +[INFO|configuration_utils.py:816] 2026-07-05 18:52:56,537 >> Configuration saved in demo/output/onereason_0.8b_sft/checkpoint-400/generation_config.json +{'loss': '3.009', 'grad_norm': '29.62', 'learning_rate': '6.667e-06', 'epoch': '0.01252'} +{'loss': '2.548', 'grad_norm': '8.812', 'learning_rate': '1.5e-05', 'epoch': '0.02505'} +{'loss': '2.234', 'grad_norm': '4.938', 'learning_rate': '2e-05', 'epoch': '0.03757'} +{'loss': '2.058', 'grad_norm': '3.016', 'learning_rate': '1.998e-05', 'epoch': '0.05009'} +{'loss': '1.851', 'grad_norm': '3.094', 'learning_rate': '1.995e-05', 'epoch': '0.06262'} +{'loss': '1.856', 'grad_norm': '3.266', 'learning_rate': '1.991e-05', 'epoch': '0.07514'} +{'loss': '1.752', 'grad_norm': '2.359', 'learning_rate': '1.984e-05', 'epoch': '0.08766'} +{'loss': '1.811', 'grad_norm': '2.922', 'learning_rate': '1.976e-05', 'epoch': '0.1002'} +{'loss': '1.698', 'grad_norm': '2.594', 'learning_rate': '1.967e-05', 'epoch': '0.1127'} +{'loss': '1.668', 'grad_norm': '2.344', 'learning_rate': '1.955e-05', 'epoch': '0.1252'} +{'loss': '1.654', 'grad_norm': '2.219', 'learning_rate': '1.943e-05', 'epoch': '0.1378'} +{'loss': '1.629', 'grad_norm': '2.109', 'learning_rate': '1.928e-05', 'epoch': '0.1503'} +{'loss': '1.589', 'grad_norm': '2.141', 'learning_rate': '1.913e-05', 'epoch': '0.1628'} +{'loss': '1.643', 'grad_norm': '2.391', 'learning_rate': '1.895e-05', 'epoch': '0.1753'} +{'loss': '1.575', 'grad_norm': '2.594', 'learning_rate': '1.877e-05', 'epoch': 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+[INFO|tokenization_utils_base.py:3278] 2026-07-05 18:52:57,495 >> chat template saved in demo/output/onereason_0.8b_sft/checkpoint-400/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-05 18:52:57,515 >> tokenizer config file saved in demo/output/onereason_0.8b_sft/checkpoint-400/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-05 18:52:59,470 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 400/400 [54:13<00:00, 6.45s/it] 100%|██████████| 400/400 [54:13<00:00, 8.13s/it] +[INFO|trainer.py:3815] 2026-07-05 18:52:59,471 >> Saving model checkpoint to demo/output/onereason_0.8b_sft +[INFO|configuration_utils.py:533] 2026-07-05 18:52:59,473 >> Configuration saved in demo/output/onereason_0.8b_sft/config.json +[INFO|configuration_utils.py:816] 2026-07-05 18:52:59,473 >> Configuration saved in demo/output/onereason_0.8b_sft/generation_config.json +{'train_runtime': '3253', 'train_samples_per_second': '0.491', 'train_steps_per_second': '0.123', 'train_loss': '1.57', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in demo/output/onereason_0.8b_sft/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-05 18:53:00,439 >> chat template saved in demo/output/onereason_0.8b_sft/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-05 18:53:00,459 >> tokenizer config file saved in demo/output/onereason_0.8b_sft/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 178491204GF + train_loss = 1.5697 + train_runtime = 0:54:13.17 + train_samples_per_second = 0.491 + train_steps_per_second = 0.123 +Figure saved at: demo/output/onereason_0.8b_sft/training_loss.png +[WARNING|2026-07-05 18:53:00] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-05 18:53:00] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-05 18:53:00,595 >> 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..8cf90bd --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.9165347193492685e+17, + "train_loss": 1.5697158980369568, + "train_runtime": 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