From 9b51b85a4022a886136a110330336cceb7abdc75 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 17 Jul 2026 22:37:14 +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/v15_user_logic_json_lr15e6 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 + ...44154.ubuntu-System-Product-Name.3834352.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2401 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 174 ++ trainer_state.json | 1254 +++++++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 45390 bytes 16 files changed, 4131 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/Jul08_01-09-14_ubuntu-System-Product-Name/events.out.tfevents.1783444154.ubuntu-System-Product-Name.3834352.0 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 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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..0b48885 --- /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: v15_user_logic_json_lr15e6 + results: [] +--- + + + +# v15_user_logic_json_lr15e6 + +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 exp_user_logic_json 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: 1.5e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607151 +- 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..b5ae52c --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 4.2033452439454925e+17, + "train_loss": 1.4887716360433154, + "train_runtime": 7587.3157, + "train_samples_per_second": 0.457, + "train_steps_per_second": 0.114 +} \ 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..5605651 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1cd40caea8b9d89575d3de73996ead0b0187fd69661dfd9486836a4dba85fa0 +size 1602902832 diff --git a/runs/Jul08_01-09-14_ubuntu-System-Product-Name/events.out.tfevents.1783444154.ubuntu-System-Product-Name.3834352.0 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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..46c7dd4 --- /dev/null +++ b/train.log @@ -0,0 +1,2401 @@ +/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|2026-07-08 01:08:23] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:771] 2026-07-08 01:08:24,053 >> 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] 2026-07-08 01:08:24,059 >> 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] 2026-07-08 01:08:28,617 >> 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] 2026-07-08 01:08:28,620 >> 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] 2026-07-08 01:08:29,098 >> 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] 2026-07-08 01:08:29,101 >> 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|2026-07-08 01:08:31] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/user_logic_json.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: 7258 examples [00:00, 68128.18 examples/s] Generating train split: 20026 examples [00:00, 76464.26 examples/s] Generating train split: 31963 examples [00:00, 75693.99 examples/s] Generating train split: 42007 examples [00:00, 81475.76 examples/s] Generating train split: 45352 examples [00:00, 79157.80 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/45352 [00:00user +【用户交互历史】： +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [直播-关注] <|living_begin|> +【2025-05-06】 + --:-- [直播-关注] <|living_begin|> +【2025-05-14】 + --:-- [直播-关注] <|living_begin|> +【2025-05-31】 + --:-- [直播-关注] <|living_begin|> +【2025-06-05】 + --:-- [直播-关注] <|living_begin|> +【2025-06-10】 + --:-- [直播-关注] <|living_begin|> +【2025-06-11】 + --:-- [直播-关注] <|living_begin|> +【2025-06-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注] <|living_begin|> +【2025-07-06】 + --:-- [直播-关注] <|living_begin|> +【2025-07-12】 + --:-- [直播-关注] <|living_begin|> +【2025-07-13】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [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|> +【2025-12-16】 + 13:31 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:01 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:09 [广告-深度转化] <|ad_begin|> + 08:12 [广告-深度转化] <|ad_begin|> + 08:18 [广告-深度转化] <|ad_begin|> + 08:55 [广告-深度转化] <|ad_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:19 [广告-深度转化] <|ad_begin|> + 07:59 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 09:42 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 08:39 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 07:23 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 09:50 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:34 [广告-深度转化] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:31 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 08:46 [广告-深度转化] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 23:26 [广告-深度转化] <|ad_begin|> + 23:27 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:21 [广告-深度转化] <|ad_begin|> + 03:25 [广告-深度转化] <|ad_begin|> + 03:27 [广告-深度转化] <|ad_begin|> + 08:36 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:21 [广告-深度转化] <|ad_begin|> + 02:31 [广告-深度转化] <|ad_begin|> + 05:15 [广告-深度转化] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 10:44 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:20 [广告-点击] <|ad_begin|> + 03:24 [广告-深度转化] <|ad_begin|> + 03:24 [广告-点击] <|ad_begin|> + 08:34 [广告-点击] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> + 23:48 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:24 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 09:14 [广告-深度转化] <|ad_begin|> + 09:14 [广告-点击] <|ad_begin|> + 09:46 [广告-点击] <|ad_begin|> + 10:35 [广告-点击] <|ad_begin|> + 10:44 [广告-点击] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 23:31 [广告-点击] <|ad_begin|> + 23:39 [广告-点击] <|ad_begin|> + 23:43 [广告-点击] <|ad_begin|> + 23:44 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:21 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 02:16 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:06 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-11】 + --:-- [直播-关注/首次打赏] <|living_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|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:41 [广告-点击] <|ad_begin|> + 13:50 [广告-深度转化] <|ad_begin|> + 13:50 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 00:31 [广告-深度转化] <|ad_begin|> + 00:31 [广告-点击] <|ad_begin|> + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:24 [广告-深度转化] <|ad_begin|> + 07:24 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 02:56 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 23:18 [广告-点击] <|ad_begin|> +【2026-01-20】 + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 10:18 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:21 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 00:51 [视频-收藏/长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 10:15 [视频-转发/长播] <|video_begin|> + 10:42 [视频-收藏/长播] <|video_begin|> + 10:51 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-关注/长播] <|video_begin|> + 12:32 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + --:-- [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|> + 04:32 [广告-点击] <|ad_begin|> + 04:48 [视频-点赞/长播] <|video_begin|> + 06:17 [视频-点赞/长播] <|video_begin|> + 06:23 [视频-评论/长播] <|video_begin|> + 22:15 [视频-浏览] <|video_begin|> +【2026-01-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:20 [广告-深度转化] <|ad_begin|> + 01:20 [广告-点击] <|ad_begin|> + 21:51 [广告-深度转化] <|ad_begin|> + 21:51 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:33 [广告-点击] <|ad_begin|> + 08:05 [视频-转发/长播] <|video_begin|> + 11:18 [视频-转发/长播] <|video_begin|> + 11:28 [视频-点赞/长播] <|video_begin|> + 22:05 [视频-收藏/长播] <|video_begin|> +【2026-01-26】 + 00:37 [视频-点赞/长播] <|video_begin|> + 12:18 [视频-收藏/长播] <|video_begin|> + 12:21 [视频-收藏/长播] <|video_begin|> + 12:46 [视频-转发/收藏/长播] <|video_begin|> + 13:58 [视频-收藏/长播] <|video_begin|> + 22:21 [视频-收藏/长播] <|video_begin|> + 23:08 [视频-收藏/长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:19 [广告-深度转化] <|ad_begin|> + 01:19 [广告-点击] <|ad_begin|> + 02:46 [视频-评论/长播] <|video_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> + 11:44 [视频-转发/长播] <|video_begin|> + 21:51 [视频-点赞/长播] <|video_begin|> + 22:17 [视频-收藏/长播] <|video_begin|> + 23:56 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:34 [视频-收藏/长播] <|video_begin|> + 12:39 [广告-点击] <|ad_begin|> + 22:09 [广告-深度转化] <|ad_begin|> + 22:09 [广告-点击] <|ad_begin|> + 22:15 [广告-深度转化] <|ad_begin|> + 22:15 [广告-点击] <|ad_begin|> + 23:16 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 09:29 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 08:06 [广告-深度转化] <|ad_begin|> + 08:06 [广告-点击] <|ad_begin|> + 08:38 [视频-评论/长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + 00:00 [广告-深度转化] <|ad_begin|> + 00:00 [广告-点击] <|ad_begin|> + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 09:46 [视频-关注/长播] <|video_begin|> + 12:12 [视频-点赞/长播] <|video_begin|> + 12:19 [视频-评论/长播] <|video_begin|> + 13:21 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 10:17 [视频-转发/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 01:52 [广告-深度转化] <|ad_begin|> + 01:52 [广告-点击] <|ad_begin|> + 03:03 [广告-深度转化] <|ad_begin|> + 03:03 [广告-点击] <|ad_begin|> + 04:31 [视频-评论/长播] <|video_begin|> + 07:25 [视频-评论/长播] <|video_begin|> + 11:50 [视频-收藏/长播] <|video_begin|> + 12:14 [广告-点击] <|ad_begin|> + 13:29 [视频-转发/长播] <|video_begin|> + 13:34 [广告-点击] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> + 23:08 [视频-转发/长播] <|video_begin|> +【2026-02-03】 + 01:35 [广告-点击] <|ad_begin|> + 11:45 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 22:17 [视频-关注/长播] <|video_begin|> + 22:37 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [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|> + 08:34 [视频-收藏/长播] <|video_begin|> + 08:39 [视频-关注/评论/长播] <|video_begin|> + 21:56 [视频-收藏/长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:21 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 04:39 [广告-深度转化] <|ad_begin|> + 04:39 [广告-点击] <|ad_begin|> + 05:11 [视频-长播] <|video_begin|> + 05:52 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 07:58 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:58 [视频-长播] <|video_begin|> + 11:43 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 11:58 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 14:01 [视频-关注/长播] <|video_begin|> + 14:04 [视频-关注/长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-关注/长播] <|video_begin|> + 14:10 [视频-长播] <|video_begin|> + 14:10 [视频-关注/长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:21 [视频-长播] <|video_begin|> + 14:21 [视频-关注/长播] <|video_begin|> + 14:22 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_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 +核心判定逻辑（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 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +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：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请面向以下主题提取行为逻辑链： +主题：从泛化兴趣到专业化聚焦的收藏品认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化兴趣到专业化聚焦的收藏品认知演化", + "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" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化兴趣到专业化聚焦的收藏品认知演化", "events": [{"date": "2025-12-17", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始触发：被动接收大量泛化的“老物件上门回收”广告，形成初步认知。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣聚焦：从泛化的“老物件”概念，收敛到对特定品类“古钱币”的价值和历史产生主动兴趣，并参与评论互动。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知跃迁：兴趣从“物品本身”深化至“专业交易场景”，关注点转向“拍卖会”这一专业变现或投资渠道，完成了从了解到实践的认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-27】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-17】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-26】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-11】 + --:-- [直播-关注] <|living_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [直播-关注] <|living_begin|> +【2025-10-14】 + --:-- [直播-关注] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> +【2025-11-16】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [直播-关注] <|living_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-26】 + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [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|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [直播-关注] <|living_begin|> +【2025-12-22】 + 22:31 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + 12:19 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 02:43 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:14 [广告-深度转化] <|ad_begin|> + 11:57 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 18:09 [广告-深度转化] <|ad_begin|> + 21:01 [视频-关注/长播] <|video_begin|> +【2026-01-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:09 [视频-关注/长播] <|video_begin|> + 18:24 [广告-深度转化] <|ad_begin|> + 20:45 [视频-评论/长播] <|video_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + 02:32 [视频-转发/点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:21 [视频-关注/长播] <|video_begin|> + 07:06 [视频-关注/长播] <|video_begin|> + 07:12 [视频-关注/长播] <|video_begin|> + 07:12 [视频-关注/长播] <|video_begin|> + 11:35 [视频-关注/长播] <|video_begin|> + 13:10 [视频-评论/长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 01:12 [广告-深度转化] <|ad_begin|> + 01:56 [视频-评论/点赞/长播] <|video_begin|> + 03:43 [视频-评论/长播] <|video_begin|> + 06:12 [视频-点赞/长播] <|video_begin|> + 06:32 [视频-关注/长播] <|video_begin|> + 06:54 [视频-关注/长播] <|video_begin|> + 08:12 [视频-点赞/长播] <|video_begin|> + 08:36 [视频-关注/长播] <|video_begin|> + 10:57 [视频-浏览] <|video_begin|> + 11:39 [视频-关注/长播] <|video_begin|> + 12:08 [视频-关注/长播] <|video_begin|> + 19:03 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + 02:20 [广告-深度转化] <|ad_begin|> + 04:52 [视频-关注/长播] <|video_begin|> + 05:12 [视频-关注/长播] <|video_begin|> + 06:04 [视频-评论/长播] <|video_begin|> + 06:11 [视频-关注/长播] <|video_begin|> + 10:02 [视频-关注/长播] <|video_begin|> + 10:14 [视频-点赞/长播] <|video_begin|> + 10:19 [视频-关注/长播] <|video_begin|> + 12:16 [视频-转发/长播] <|video_begin|> + 15:36 [视频-评论/长播] <|video_begin|> + 19:16 [视频-评论/长播] <|video_begin|> + 19:39 [视频-点赞/长播] <|video_begin|> + 21:41 [视频-关注/长播] <|video_begin|> + 22:12 [视频-点赞/长播] <|video_begin|> + 22:49 [视频-评论/长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:08 [视频-点赞/长播] <|video_begin|> + 02:11 [视频-评论/长播] <|video_begin|> + 02:11 [广告-深度转化] <|ad_begin|> + 04:06 [视频-关注/长播] <|video_begin|> + 04:28 [视频-点赞/长播] <|video_begin|> + 04:34 [视频-关注/长播] <|video_begin|> + 05:14 [视频-关注/长播] <|video_begin|> + 10:07 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 01:15 [视频-点赞/长播] <|video_begin|> + 02:01 [广告-深度转化] <|ad_begin|> + 07:42 [视频-点赞/长播] <|video_begin|> + 08:41 [视频-浏览] <|video_begin|> +【2026-01-29】 + 01:15 [视频-关注/长播] <|video_begin|> + 04:53 [广告-深度转化] <|ad_begin|> + 05:01 [视频-收藏/长播] <|video_begin|> + 07:17 [视频-关注/长播] <|video_begin|> + 07:21 [视频-关注/长播] <|video_begin|> + 07:30 [视频-收藏/长播] <|video_begin|> + 13:31 [视频-关注/长播] <|video_begin|> +【2026-01-30】 + 02:02 [广告-深度转化] <|ad_begin|> + 03:54 [广告-深度转化] <|ad_begin|> + 04:41 [广告-深度转化] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 13:02 [视频-关注/长播] <|video_begin|> + 13:09 [广告-深度转化] <|ad_begin|> + 20:56 [广告-深度转化] <|ad_begin|> + 21:26 [广告-深度转化] <|ad_begin|> + 21:32 [广告-深度转化] <|ad_begin|> + 21:35 [广告-深度转化] <|ad_begin|> + 21:35 [广告-点击] <|ad_begin|> + 23:49 [视频-关注/长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + 01:03 [视频-关注/长播] <|video_begin|> + 01:15 [广告-深度转化] <|ad_begin|> + 01:15 [广告-点击] <|ad_begin|> + 01:18 [广告-深度转化] <|ad_begin|> + 01:18 [广告-点击] <|ad_begin|> + 01:18 [广告-点击] <|ad_begin|> + 01:19 [广告-深度转化] <|ad_begin|> + 01:19 [广告-点击] <|ad_begin|> + 02:09 [广告-深度转化] <|ad_begin|> + 02:09 [广告-点击] <|ad_begin|> + 02:11 [广告-深度转化] <|ad_begin|> + 02:11 [广告-点击] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 02:51 [广告-深度转化] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 02:52 [广告-点击] <|ad_begin|> + 02:52 [广告-点击] <|ad_begin|> + 02:55 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:40 [广告-深度转化] <|ad_begin|> + 06:40 [广告-点击] <|ad_begin|> + 09:28 [视频-点赞/长播] <|video_begin|> + 10:42 [视频-关注/长播] <|video_begin|> + 14:22 [视频-点赞/长播] <|video_begin|> + 16:58 [广告-深度转化] <|ad_begin|> + 16:58 [广告-点击] <|ad_begin|> + 17:08 [广告-深度转化] <|ad_begin|> + 17:08 [广告-点击] <|ad_begin|> + 17:08 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:14 [广告-深度转化] <|ad_begin|> + 08:14 [广告-点击] <|ad_begin|> + 08:14 [广告-深度转化] <|ad_begin|> + 08:14 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 08:16 [广告-点击] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 08:16 [广告-点击] <|ad_begin|> + 08:17 [广告-点击] <|ad_begin|> + 08:20 [广告-点击] <|ad_begin|> + 08:20 [广告-深度转化] <|ad_begin|> + 08:20 [广告-点击] <|ad_begin|> + 08:20 [广告-点击] <|ad_begin|> + 08:21 [广告-深度转化] <|ad_begin|> + 08:21 [广告-点击] <|ad_begin|> + 09:26 [视频-关注/长播] <|video_begin|> + 09:31 [广告-深度转化] <|ad_begin|> + 09:31 [广告-点击] <|ad_begin|> + 09:31 [广告-深度转化] <|ad_begin|> + 09:31 [广告-点击] <|ad_begin|> + 09:32 [广告-深度转化] <|ad_begin|> + 09:32 [广告-点击] <|ad_begin|> + 09:35 [广告-深度转化] <|ad_begin|> + 09:35 [广告-点击] <|ad_begin|> + 09:35 [广告-深度转化] <|ad_begin|> + 09:35 [广告-点击] <|ad_begin|> + 09:36 [广告-点击] <|ad_begin|> + 10:49 [广告-点击] <|ad_begin|> + 23:35 [广告-点击] <|ad_begin|> +【2026-02-02】 + 01:49 [广告-点击] <|ad_begin|> + 01:49 [广告-点击] <|ad_begin|> + 06:51 [视频-关注/长播] <|video_begin|> + 07:03 [广告-点击] <|ad_begin|> + 08:01 [视频-关注/长播] <|video_begin|> + 08:09 [视频-评论/长播] <|video_begin|> + 10:00 [视频-评论/长播] <|video_begin|> + 10:41 [视频-评论/长播] <|video_begin|> + 11:50 [视频-点赞/长播] <|video_begin|> + 11:55 [视频-点赞/长播] <|video_begin|> + 17:24 [广告-点击] <|ad_begin|> + 18:21 [广告-深度转化] <|ad_begin|> + 18:21 [广告-点击] <|ad_begin|> + 18:21 [广告-深度转化] <|ad_begin|> + 18:21 [广告-点击] <|ad_begin|> + 18:21 [广告-深度转化] <|ad_begin|> + 18:21 [广告-点击] <|ad_begin|> + 18:46 [视频-收藏/长播] <|video_begin|> + 21:22 [视频-收藏/长播] <|video_begin|> +【2026-02-03】 + 02:54 [广告-深度转化] <|ad_begin|> + 02:54 [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-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|> + 03:37 [视频-关注/长播] <|video_begin|> + 04:34 [广告-点击] <|ad_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:56 [广告-深度转化] <|ad_begin|> + 04:56 [广告-点击] <|ad_begin|> + 09:28 [视频-点赞/长播] <|video_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 09:39 [视频-点赞/长播] <|video_begin|> + 09:40 [视频-点赞/长播] <|video_begin|> + 09:45 [视频-关注/长播] <|video_begin|> + 13:05 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + 22:36 [视频-评论/长播] <|video_begin|> + 23:20 [视频-评论/长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 00:56 [广告-点击] <|ad_begin|> + 01:13 [广告-深度转化] <|ad_begin|> + 01:13 [广告-点击] <|ad_begin|> + 01:54 [广告-深度转化] <|ad_begin|> + 01:54 [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-点击] <|prod_begin|> + 03:46 [视频-点赞/长播] <|video_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 04:52 [广告-点击] <|ad_begin|> + 04:54 [广告-深度转化] <|ad_begin|> + 04:54 [广告-点击] <|ad_begin|> + 04:54 [广告-深度转化] <|ad_begin|> + 04:54 [广告-点击] <|ad_begin|> + 05:01 [广告-点击] <|ad_begin|> + 13:23 [视频-评论/长播] <|video_begin|> + 14:09 [视频-关注/长播] <|video_begin|> + 14:22 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-转发/长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> + 20:47 [视频-转发/长播] <|video_begin|> + 22:04 [广告-点击] <|ad_begin|> + 22:27 [广告-点击] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> + 22:56 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:54 [视频-长播] <|video_begin|> + 00:57 [广告-深度转化] <|ad_begin|> + 00:57 [广告-点击] <|ad_begin|> + 01:56 [视频-长播] <|video_begin|> + 02:04 [广告-深度转化] <|ad_begin|> + 02:04 [广告-点击] <|ad_begin|> + 02:17 [视频-长播] <|video_begin|> + 02:17 [视频-长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:46 [视频-长播] <|video_begin|> + 02:46 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:42 [视频-关注/长播] <|video_begin|> + 03:43 [视频-长播] <|video_begin|> + 03:43 [视频-长播] <|video_begin|> + 03:47 [视频-长播] <|video_begin|> + 03:58 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-关注/点赞/长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 04:30 [视频-长播] <|video_begin|> + 04:30 [视频-长播] <|video_begin|> + 04:43 [视频-关注/长播] <|video_begin|> + 05:56 [视频-长播] <|video_begin|> + 06:08 [视频-长播] <|video_begin|> + 06:08 [视频-关注/长播] <|video_begin|> + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 07:29 [视频-长播] <|video_begin|> + 07:29 [视频-长播] <|video_begin|> + 07:37 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:51 [视频-关注/长播] <|video_begin|> + 09:01 [视频-长播] <|video_begin|> + 09:01 [视频-长播] <|video_begin|> + 09:05 [视频-长播] <|video_begin|> + 09:05 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-长播] <|video_begin|> + 09:13 [视频-点赞/长播] <|video_begin|> + 09:21 [视频-长播] <|video_begin|> + 09:21 [视频-长播] <|video_begin|> + 09:21 [视频-长播] <|video_begin|> + 09:21 [视频-长播] <|video_begin|> + 10:17 [视频-长播] <|video_begin|> + 10:17 [视频-长播] <|video_begin|> + 10:19 [视频-长播] <|video_begin|> + 10:22 [视频-长播] <|video_begin|> + 10:22 [视频-长播] <|video_begin|> + 10:33 [广告-点击] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + 11:51 [视频-长播] <|video_begin|> + 11:51 [视频-长播] <|video_begin|> + 11:51 [视频-长播] <|video_begin|> + 14:05 [视频-长播] <|video_begin|> + 14:05 [视频-长播] <|video_begin|> + 14:13 [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|> +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）： 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)： +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 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：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请面向以下主题提取行为逻辑链： +主题：从被动信息接收到主动寻求专业解决方案的兴趣深化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动信息接收到主动寻求专业解决方案的兴趣深化", + "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": "从被动信息接收到主动寻求专业解决方案的兴趣深化", "events": [{"date": "2026-01-21", "action": "[视频-关注/长播] <|video_begin|>", "logic": "兴趣触发：开始被动接收并关注具体的汽车型号信息，对车辆的性能、经济性等参数产生初步认知。"}, {"date": "2026-01-23", "action": "[视频-关注/长播] <|video_begin|>", "logic": "信息深化：兴趣从新车转向二手车市场，开始了解交易流程、验车等更深层次的专业知识，表明购车意图在增强，并进入信息调研阶段。"}, {"date": "2026-02-04", "action": "[视频-关注/长播] <|video_begin|>", "logic": "路径修正与决策收敛：在密集浏览各类购车、验车信息后，用户的行为最终收敛到“租车/包车”服务。这表明用户在权衡利弊后，放弃了直接购车的想法，转向一种更灵活、专业的出行解决方案，完成了从“拥有权”到“使用权”的认知转变。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-09-24】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2023-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2023-10-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2023-11-19】 + --:-- [商品-购买] <|prod_begin|> +【2023-12-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2023-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-07-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-07-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-11】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-01-15】 + --:-- [直播-关注] <|living_begin|> +【2025-01-16】 + --:-- [直播-关注/打赏] <|living_begin|> +【2025-01-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> +【2025-01-31】 + --:-- [直播-关注] <|living_begin|> +【2025-02-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-02-11】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-02-14】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> +【2025-02-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-02-20】 + --:-- [直播-关注] <|living_begin|> +【2025-03-05】 + --:-- [直播-关注] <|living_begin|> +【2025-03-26】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-12】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-20】 + --:-- [直播-关注/打赏] <|living_begin|> +【2025-04-26】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-12】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-14】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-16】 + --:-- [直播-关注] <|living_begin|> +【2025-05-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-02】 + --:-- [直播-打赏] <|living_begin|> +【2025-07-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-04】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-17】 + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-06】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-10-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-07】 + --:-- [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|> +【2025-11-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-27】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + --:-- [商品-点击] <|prod_begin|> + 17:27 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + --:-- [直播-首次打赏] <|living_begin|> + 15:32 [广告-深度转化] <|ad_begin|> + 19:41 [广告-深度转化] <|ad_begin|> + 23:00 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + 21:45 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 10:29 [广告-深度转化] <|ad_begin|> + 21:56 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-22】 + 18:08 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + 20:28 [广告-深度转化] <|ad_begin|> + 20:40 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-26】 + 22:10 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-28】 + 21:05 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:10 [广告-点击] <|ad_begin|> + 06:38 [广告-深度转化] <|ad_begin|> + 06:38 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 07:01 [广告-点击] <|ad_begin|> + 08:07 [广告-点击] <|ad_begin|> + 08:41 [广告-点击] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-02】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 06:56 [广告-点击] <|ad_begin|> + 06:57 [广告-深度转化] <|ad_begin|> + 06:57 [广告-点击] <|ad_begin|> +【2026-01-04】 + 01:16 [广告-深度转化] <|ad_begin|> + 01:16 [广告-点击] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 19:07 [广告-深度转化] <|ad_begin|> + 19:07 [广告-点击] <|ad_begin|> + 19:51 [广告-深度转化] <|ad_begin|> + 19:51 [广告-点击] <|ad_begin|> + 20:13 [广告-深度转化] <|ad_begin|> + 20:13 [广告-点击] <|ad_begin|> +【2026-01-05】 + 01:05 [广告-深度转化] <|ad_begin|> + 01:05 [广告-点击] <|ad_begin|> + 03:54 [广告-点击] <|ad_begin|> + 03:56 [广告-深度转化] <|ad_begin|> + 03:56 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 18:10 [广告-深度转化] <|ad_begin|> + 18:10 [广告-点击] <|ad_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 17:23 [广告-点击] <|ad_begin|> + 17:40 [广告-深度转化] <|ad_begin|> + 17:40 [广告-点击] <|ad_begin|> +【2026-01-14】 + 02:18 [广告-深度转化] <|ad_begin|> + 02:18 [广告-点击] <|ad_begin|> + 02:30 [广告-深度转化] <|ad_begin|> + 02:30 [广告-点击] <|ad_begin|> +【2026-01-15】 + 17:30 [广告-点击] <|ad_begin|> + 18:58 [广告-深度转化] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + 18:16 [广告-点击] <|ad_begin|> + 18:23 [广告-深度转化] <|ad_begin|> + 18:23 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 20:05 [广告-深度转化] <|ad_begin|> + 20:05 [广告-点击] <|ad_begin|> + 20:12 [广告-点击] <|ad_begin|> + 20:13 [广告-点击] <|ad_begin|> + 21:50 [广告-点击] <|ad_begin|> +【2026-01-21】 + 17:02 [广告-深度转化] <|ad_begin|> + 17:02 [广告-点击] <|ad_begin|> + 17:39 [广告-点击] <|ad_begin|> + 17:40 [视频-点赞/长播] <|video_begin|> + 17:40 [视频-浏览] <|video_begin|> + 18:13 [广告-点击] <|ad_begin|> + 21:56 [广告-点击] <|ad_begin|> + 22:22 [视频-浏览] <|video_begin|> +【2026-01-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-23】 + 18:54 [视频-点赞/长播] <|video_begin|> + 19:25 [广告-点击] <|ad_begin|> + 19:31 [广告-深度转化] <|ad_begin|> + 19:31 [广告-点击] <|ad_begin|> +【2026-01-24】 + 13:07 [视频-点赞/长播] <|video_begin|> + 16:28 [视频-点赞/长播] <|video_begin|> + 16:28 [视频-评论/长播] <|video_begin|> + 17:11 [视频-点赞/长播] <|video_begin|> + 18:58 [视频-评论/长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 01:21 [视频-点赞/长播] <|video_begin|> + 09:17 [广告-深度转化] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 14:38 [视频-点赞/长播] <|video_begin|> + 19:24 [广告-点击] <|ad_begin|> + 19:54 [广告-深度转化] <|ad_begin|> + 19:54 [广告-点击] <|ad_begin|> + 19:54 [广告-点击] <|ad_begin|> + 19:55 [广告-点击] <|ad_begin|> + 19:56 [广告-点击] <|ad_begin|> + 19:58 [广告-点击] <|ad_begin|> + 20:00 [广告-点击] <|ad_begin|> + 20:01 [广告-点击] <|ad_begin|> + 20:03 [广告-点击] <|ad_begin|> + 20:08 [广告-深度转化] <|ad_begin|> + 20:08 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:42 [视频-点赞/长播] <|video_begin|> + 22:05 [广告-点击] <|ad_begin|> + 22:10 [广告-深度转化] <|ad_begin|> + 22:10 [广告-点击] <|ad_begin|> + 23:18 [广告-点击] <|ad_begin|> + 23:58 [广告-点击] <|ad_begin|> +【2026-01-27】 + 00:03 [广告-点击] <|ad_begin|> + 19:44 [广告-点击] <|ad_begin|> + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> + 21:17 [广告-点击] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> + 22:36 [广告-点击] <|ad_begin|> + 23:10 [视频-评论/长播] <|video_begin|> + 23:34 [广告-深度转化] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 00:10 [广告-点击] <|ad_begin|> + 19:37 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 08:53 [视频-点赞/长播] <|video_begin|> + 15:22 [视频-点赞/长播] <|video_begin|> + 16:15 [视频-评论/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 07:05 [视频-点赞/长播] <|video_begin|> + 07:05 [视频-转发/长播] <|video_begin|> + 18:26 [广告-深度转化] <|ad_begin|> + 18:26 [广告-点击] <|ad_begin|> + 18:49 [广告-点击] <|ad_begin|> + 21:50 [广告-深度转化] <|ad_begin|> + 21:50 [广告-点击] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> + 22:06 [视频-评论/长播] <|video_begin|> +【2026-01-31】 + 18:17 [广告-点击] <|ad_begin|> + 18:20 [广告-深度转化] <|ad_begin|> + 18:20 [广告-点击] <|ad_begin|> +【2026-02-01】 + 11:31 [视频-点赞/长播] <|video_begin|> + 12:16 [视频-点赞/长播] <|video_begin|> + 15:21 [视频-点赞/长播] <|video_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 15:35 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 04:17 [视频-点赞/长播] <|video_begin|> + 07:38 [视频-评论/点赞/长播] <|video_begin|> + 18:37 [广告-点击] <|ad_begin|> + 20:20 [广告-深度转化] <|ad_begin|> + 20:20 [广告-点击] <|ad_begin|> +【2026-02-03】 + 13:08 [视频-点赞/长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 15:17 [视频-长播] <|video_begin|> + 17:17 [视频-长播] <|video_begin|> + 17:17 [视频-长播] <|video_begin|> + 17:29 [视频-长播] <|video_begin|> + 19:15 [广告-深度转化] <|ad_begin|> + 19:15 [广告-点击] <|ad_begin|> + 19:28 [广告-点击] <|ad_begin|> + 20:11 [视频-长播] <|video_begin|> + 20:13 [视频-长播] <|video_begin|> + 20:26 [视频-长播] <|video_begin|> + 20:28 [视频-长播] <|video_begin|> + 20:28 [视频-长播] <|video_begin|> + 20:28 [视频-长播] <|video_begin|> + 20:50 [广告-深度转化] <|ad_begin|> + 20:50 [广告-点击] <|ad_begin|> + 20:53 [视频-长播] <|video_begin|> + 21:24 [视频-长播] <|video_begin|> + 21:35 [视频-长播] <|video_begin|> + 21:39 [视频-长播] <|video_begin|> + 21:39 [视频-长播] <|video_begin|> + 21:42 [视频-长播] <|video_begin|> + 21:42 [视频-长播] <|video_begin|> + 21:51 [视频-长播] <|video_begin|> + 21:51 [视频-长播] <|video_begin|> + 22:04 [视频-长播] <|video_begin|> + 22:04 [视频-长播] <|video_begin|> +【2026-02-04】 + 03:22 [视频-长播] <|video_begin|> + 03:46 [广告-点击] <|ad_begin|> + 03:46 [视频-长播] <|video_begin|> + 03:53 [广告-点击] <|ad_begin|> + 03:53 [视频-长播] <|video_begin|> + 03:53 [视频-长播] <|video_begin|> + 03:53 [视频-长播] <|video_begin|> + 18:23 [视频-长播] <|video_begin|> + 21:21 [视频-长播] <|video_begin|> + 21:21 [视频-长播] <|video_begin|> + 21:21 [视频-长播] <|video_begin|> + 21:21 [视频-评论/长播] <|video_begin|> + 21:31 [视频-长播] <|video_begin|> + 21:31 [视频-长播] <|video_begin|> + 21:31 [视频-长播] <|video_begin|> + 21:37 [视频-长播] <|video_begin|> + 22:41 [视频-长播] <|video_begin|> + 23:04 [视频-长播] <|video_begin|> + 23:04 [视频-长播] <|video_begin|> + 23:04 [视频-长播] <|video_begin|> +【2026-02-05】 + 07:22 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 12:21 [视频-长播] <|video_begin|> + 12:25 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 15:12 [视频-长播] <|video_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 +核心提取逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 与 交互 A 必须在同一生活场景、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 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 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。 +请针对以下主题提取行为逻辑链： +主题：从泛化皮肤问题到聚焦特定药品成分的诊疗收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化皮肤问题到聚焦特定药品成分的诊疗收敛", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/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分析。 + +我接到的任务是分析用户的行为，挖掘出一条从泛化皮肤问题到聚焦特定药品成分的诊疗收敛过程的行为链。 + +在仔细梳理了这位用户的完整交互时间线后，我发现他的行为模式非常清晰。在2025年10月底之前，他的消费主要集中在男性服饰、鞋履、车载用品和一些生活日用品上，比如购买了多款运动鞋 <|prod_begin|>、<|prod_begin|> 和保暖衣物 <|prod_begin|>、<|prod_begin|>，展现了一个典型的中年男性消费画像。然而，从2025年10月下旬开始，我观察到了一些新的兴趣点。 + +我的分析起点是2025年11月5日，这一天他首次点击了一款名为“葵花医用皮肤液体敷料”的商品 <|prod_begin|>。从商品标题中的“皮炎湿疹”、“皮肤脱屑红斑”等关键词来看，我判断他可能开始受到某种皮肤问题的困扰。这是一种比较泛化的求助信号，他可能还不清楚具体病因，只是想找一种通用的外用产品来缓解症状。 + +紧接着，时间来到2025年11月27日，我注意到一个关键的转变。他点击了一款名为“嘎日迪百癣夏塔热胶囊”的药品 <|prod_begin|>。与之前的外用敷料不同，这是一款口服胶囊，其适应症描述也更为广泛，涵盖了“皮肤癣”、“过敏性皮炎”、“痤疮”等多种皮肤问题。这一行为让我推断，用户可能认为单纯的外用产品效果不佳，或者他经过初步了解后，开始寻求一种能够从内部调理、更系统性地解决问题的方案。他的认知已经从简单的“外部涂抹”升级到了“内服治疗”的层面，这是一个重要的兴趣递进。 + +仅仅几天后的12月3日，他又一次点击了同一种药品“嘎日迪百癣夏塔热胶囊” <|prod_begin|>。这种在短时间内的重复点击行为，极大地增强了我的判断。这表明他并非随意浏览，而是对这款特定药品产生了强烈的意向，正在进行深入的了解和决策评估。他可能在比较价格、查看用户评价，或者在下定购买决心前的最后确认。这个行为标志着他的需求已经从宽泛的“解决皮肤问题”收敛到了“考虑购买‘百癣夏塔热胶囊’”这一非常具体的意图上。 + +综合来看，用户的行为展现了一个清晰的逻辑链条。他从一个泛化的皮肤症状出发，首先尝试了解通用的外用产品，随后将注意力转移到一种具体的、能系统性治疗的内服药品上，并对此进行了反复的关注和评估。 + +因此，我倾向于认为，用户的需求经历了从泛化到聚焦的收敛过程。这个过程始于他对皮肤问题的初步探索，通过点击葵花医用皮肤液体敷料 <|prod_begin|> 表现出来；随后，他的认知深化，转向寻求更根本的内服解决方案，体现为首次点击嘎日迪百癣夏塔热胶囊 <|prod_begin|>；最终，通过在短期内再次点击同一药品 <|prod_begin|>，完成了从初步了解到锁定特定药品的决策过程。这一系列行为清晰地勾勒出他针对皮肤问题的诊疗思路演变，未来他有较大概率会购买此类药品或继续关注相关的健康内容。 +{"logic_chain": {"name": "从泛化皮肤问题到聚焦特定药品成分的诊疗收敛", "events": [{"date": "2025-11-05", "action": "[商品-点击] <|prod_begin|>", "logic": "初始触发：用户开始关注皮肤问题，点击了针对“皮炎湿疹、脱屑红斑”的通用型外用敷料，表现出对皮肤症状的初步认知和解决意图。"}, {"date": "2025-11-27", "action": "[商品-点击] <|prod_begin|>", "logic": "认知聚焦与方案转换：用户的兴趣从外用敷料转向了具体的口服药物“百癣夏塔热胶囊”，其适应症（各类癣、皮炎、痤疮）更广谱，表明用户可能在寻求更根本的内服解决方案，实现了从“外用”到“内服”的认知跃迁。"}, {"date": "2025-12-03", "action": "[商品-点击] <|prod_begin|>", "logic": "决策强化与验证：在首次点击该药品后的一周内，用户再次点击同一药品。这种重复行为极大地增强了逻辑链的置信度，表明用户正在对该特定药品进行持续的评估和考虑，而非随机浏览，完成了从“发现”到“锁定”的决策收敛过程。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> +【2025-07-26】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-07】 + --:-- [直播-关注] <|living_begin|> +【2025-09-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + 12:31 [广告-点击] <|ad_begin|> +【2025-12-19】 + 11:35 [广告-点击] <|ad_begin|> +【2025-12-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + 10:53 [广告-点击] <|ad_begin|> +【2025-12-25】 + 14:40 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + 13:30 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> +【2026-01-03】 + 02:27 [广告-点击] <|ad_begin|> + 10:13 [广告-点击] <|ad_begin|> + 11:59 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + 00:57 [广告-点击] <|ad_begin|> + 01:30 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + 06:58 [广告-点击] <|ad_begin|> +【2026-01-14】 + 06:45 [广告-点击] <|ad_begin|> + 08:35 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 03:36 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:19 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + 12:44 [视频-关注/长播] <|video_begin|> + 13:41 [视频-点赞/长播] <|video_begin|> + 13:44 [视频-关注/转发/长播] <|video_begin|> + 14:07 [视频-收藏/长播] <|video_begin|> +【2026-01-24】 + 07:59 [视频-关注/长播] <|video_begin|> + 11:48 [视频-浏览] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:31 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 00:29 [视频-关注/长播] <|video_begin|> + 12:45 [视频-评论/长播] <|video_begin|> + 12:49 [视频-点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + 22:48 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 00:05 [视频-关注/长播] <|video_begin|> + 05:46 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + 02:34 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + 00:35 [视频-评论/长播] <|video_begin|> + 03:18 [视频-点赞/长播] <|video_begin|> + 04:14 [视频-关注/长播] <|video_begin|> + 13:22 [视频-关注/长播] <|video_begin|> + 13:50 [广告-点击] <|ad_begin|> + 22:29 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-31】 + 16:14 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-01】 + 02:28 [广告-点击] <|ad_begin|> +【2026-02-02】 + 00:17 [视频-点赞/长播] <|video_begin|> + 01:09 [广告-点击] <|ad_begin|> + 07:22 [广告-点击] <|ad_begin|> + 11:57 [视频-浏览] <|video_begin|> + 12:01 [视频-关注/长播] <|video_begin|> + 12:46 [视频-转发/长播] <|video_begin|> + 13:53 [视频-浏览] <|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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 01:31 [视频-转发/长播] <|video_begin|> + 02:02 [视频-点赞/长播] <|video_begin|> + 08:19 [视频-点赞/长播] <|video_begin|> + 12:54 [视频-关注/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|> +【2026-02-05】 + 00:03 [视频-长播] <|video_begin|> + 00:04 [视频-长播] <|video_begin|> + 00:07 [视频-长播] <|video_begin|> + 00:11 [视频-长播] <|video_begin|> + 00:14 [视频-长播] <|video_begin|> + 00:14 [视频-长播] <|video_begin|> + 01:05 [视频-长播] <|video_begin|> + 01:07 [视频-长播] <|video_begin|> + 01:07 [视频-长播] <|video_begin|> + 02:41 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:44 [视频-长播] <|video_begin|> + 02:44 [视频-长播] <|video_begin|> + 02:50 [视频-长播] <|video_begin|> + 03:06 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 09:20 [视频-长播] <|video_begin|> + 09:20 [视频-长播] <|video_begin|> + 12:14 [视频-长播] <|video_begin|> + 12:39 [视频-长播] <|video_begin|> + 12:50 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 13:17 [视频-长播] <|video_begin|> + 13:17 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:28 [视频-长播] <|video_begin|> + 13:28 [视频-长播] <|video_begin|> + 13:29 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:35 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 14:39 [视频-长播] <|video_begin|> + 14:39 [视频-长播] <|video_begin|> + 14:39 [视频-长播] <|video_begin|> + 15:34 [视频-长播] <|video_begin|> + 15:34 [视频-长播] <|video_begin|> + 15:34 [视频-长播] <|video_begin|> + 15:37 [视频-长播] <|video_begin|> + 15:46 [视频-长播] <|video_begin|> + 15:46 [视频-长播] <|video_begin|> + 15:46 [视频-长播] <|video_begin|> + 15:46 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +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 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请针对以下主题提取行为逻辑链： +主题：从功能诉求到品类聚焦：女性塑身与保暖下装的决策过程 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从功能诉求到品类聚焦：女性塑身与保暖下装的决策过程", + "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分析。 + +我接到的任务是分析一位用户在女性塑身与保暖下装方面的消费决策过程，需要从她的一系列交互行为中，梳理出一条从初步的功能诉求到最终品类聚焦的完整逻辑链。 + +在分析初期，我注意到这位用户的行为历史相当丰富，涵盖了从服饰、美妆、汽车用品到生鲜食品等多个领域，展现出多元化的生活需求。然而，在2026年2月初，我观察到了一系列高度集中的行为，这正是我此次任务的核心。 + +我的分析起点是2026年2月2日。在这一天，我发现用户点击了一款人鱼塑身衣 <|prod_begin|>。这件商品带有非常明确的功能指向——“收腰显瘦”。我认为，这标志着用户对身材塑形需求的初步显现，她开始主动探索能够优化身体曲线的功能性服装。这不仅仅是一次随意的浏览，而是她决策旅程的开端，明确了她最核心的诉求。 + +紧接着，在同一天，用户的注意力发生了有趣的迁移。她点击了一款“光腿神器” <|prod_begin|>。这个行为非常关键。光腿神器这个品类本身就是一种复合型产品，它巧妙地将保暖（加绒）、美观（仿真肤色）以及一定程度的塑形功能结合在一起。从单一、强功能的塑身衣，到这种兼顾多种场景和需求的产品，我推断用户的需求正在演化。她可能意识到，单纯的塑身衣在日常穿搭中存在局限，而她需要的是一种既能满足塑形效果，又能适应秋冬季节保暖和美观需求的综合性解决方案。 + +两天后的2月4日，用户的探索进一步深化。她点击了一款鲨鱼裤 <|prod_begin|>。与之前点击的商品相比，这款产品的描述更为具体，不仅强调了“高腰收腹提臀”的塑形功能，还特别标注了“羊毛、蚕丝”等更具体的保暖材质。这表明用户已经不满足于品类的泛泛了解，而是进入了更细致的横向对比阶段。她正在比较不同品类（如光腿神器、鲨鱼裤）在材质、保暖效果和塑形细节上的差异，试图找到最符合自己期望的产品。这个行为节点展现了她认知深化的过程，从“我需要塑形”发展到“我需要哪种材质、哪种版型的塑形保暖裤”。 + +最终，在2026年2月5日，用户的探索迎来了决策闭环。她购买了一款光腿神器 <|prod_begin|>。这款产品的描述精准地回应了她此前的所有探索点，明确标注了“保暖”、“高腰收腹”和“自然裸感”。经过对塑身衣、不同材质的鲨鱼裤和光腿神器的综合比较后，她最终选择了光腿神器作为满足其当前需求的最佳方案。这次购买行为，是她从一个宽泛的功能诉求出发，通过对不同品类的发散性探索和对比，最终收敛到一个具体品类并完成消费决策的完整体现。 + +综合来看，我认为用户的决策路径清晰地展现了一个从功能诉求到品类聚焦的演进过程。她最初由一个明确的塑形需求驱动，点击了人鱼塑身衣 <|prod_begin|>，随后将需求扩展至兼顾保暖与美观，开始关注光腿神器 <|prod_begin|>。接着，她通过点击材质和功能更细化的鲨鱼裤 <|prod_begin|> 进行深入对比，最终在充分权衡后，购买了功能全面的光腿神器 <|prod_begin|>，完成了整个决策。基于此，未来向她推荐兼具塑形、保暖和时尚设计感的下装，有较大概率被吸引。 +{"logic_chain": {"name": "从功能诉求到品类聚焦：女性塑身与保暖下装的决策过程", "events": [{"date": "2026-02-02", "action": "[商品-点击] <|prod_begin|>", "logic": "初始探索（塑形）：点击强功能性的塑身衣，明确了对“收腰显瘦”的核心诉求。"}, {"date": "2026-02-02", "action": "[商品-点击] <|prod_begin|>", "logic": "需求融合探索：开始关注“光腿神器”这一品类，该品类融合了“保暖”（加绒）、“美观”（仿真肤色）和一定的“塑形”功能，是对单一塑身衣诉求的补充和演化。"}, {"date": "2026-02-04", "action": "[商品-点击] <|prod_begin|>", "logic": "材质与功能对比：进一步点击了包含更具体材质（羊毛、蚕丝）和功能描述（高腰收腹提臀）的“鲨鱼裤”，在不同品类间进行横向对比，深化对产品细节的认知。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策收敛与闭环：在对比了多种塑形、保暖下装后，最终决策购买了一款明确标注“保暖”、“高腰收腹”的“光腿神器”，完成了从多品类发散探索到单一品类精准购买的闭环。"}]}}<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|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, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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"兴趣触发：开始被动接收并关注具体的汽车型号信息，对车辆的性能、经济性等参数产生初步认知。"}, {"date": "2026-01-23", "action": "[视频-关注/长播] <|video_begin|>", "logic": "信息深化：兴趣从新车转向二手车市场，开始了解交易流程、验车等更深层次的专业知识，表明购车意图在增强，并进入信息调研阶段。"}, {"date": "2026-02-04", "action": "[视频-关注/长播] <|video_begin|>", "logic": "路径修正与决策收敛：在密集浏览各类购车、验车信息后，用户的行为最终收敛到“租车/包车”服务。这表明用户在权衡利弊后，放弃了直接购车的想法，转向一种更灵活、专业的出行解决方案，完成了从“拥有权”到“使用权”的认知转变。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接到的任务是分析用户的行为，挖掘出一条从泛化皮肤问题到聚焦特定药品成分的诊疗收敛过程的行为链。 + +在仔细梳理了这位用户的完整交互时间线后，我发现他的行为模式非常清晰。在2025年10月底之前，他的消费主要集中在男性服饰、鞋履、车载用品和一些生活日用品上，比如购买了多款运动鞋 <|prod_begin|>、<|prod_begin|> 和保暖衣物 <|prod_begin|>、<|prod_begin|>，展现了一个典型的中年男性消费画像。然而，从2025年10月下旬开始，我观察到了一些新的兴趣点。 + +我的分析起点是2025年11月5日，这一天他首次点击了一款名为“葵花医用皮肤液体敷料”的商品 <|prod_begin|>。从商品标题中的“皮炎湿疹”、“皮肤脱屑红斑”等关键词来看，我判断他可能开始受到某种皮肤问题的困扰。这是一种比较泛化的求助信号，他可能还不清楚具体病因，只是想找一种通用的外用产品来缓解症状。 + +紧接着，时间来到2025年11月27日，我注意到一个关键的转变。他点击了一款名为“嘎日迪百癣夏塔热胶囊”的药品 <|prod_begin|>。与之前的外用敷料不同，这是一款口服胶囊，其适应症描述也更为广泛，涵盖了“皮肤癣”、“过敏性皮炎”、“痤疮”等多种皮肤问题。这一行为让我推断，用户可能认为单纯的外用产品效果不佳，或者他经过初步了解后，开始寻求一种能够从内部调理、更系统性地解决问题的方案。他的认知已经从简单的“外部涂抹”升级到了“内服治疗”的层面，这是一个重要的兴趣递进。 + +仅仅几天后的12月3日，他又一次点击了同一种药品“嘎日迪百癣夏塔热胶囊” <|prod_begin|>。这种在短时间内的重复点击行为，极大地增强了我的判断。这表明他并非随意浏览，而是对这款特定药品产生了强烈的意向，正在进行深入的了解和决策评估。他可能在比较价格、查看用户评价，或者在下定购买决心前的最后确认。这个行为标志着他的需求已经从宽泛的“解决皮肤问题”收敛到了“考虑购买‘百癣夏塔热胶囊’”这一非常具体的意图上。 + +综合来看，用户的行为展现了一个清晰的逻辑链条。他从一个泛化的皮肤症状出发，首先尝试了解通用的外用产品，随后将注意力转移到一种具体的、能系统性治疗的内服药品上，并对此进行了反复的关注和评估。 + +因此，我倾向于认为，用户的需求经历了从泛化到聚焦的收敛过程。这个过程始于他对皮肤问题的初步探索，通过点击葵花医用皮肤液体敷料 <|prod_begin|> 表现出来；随后，他的认知深化，转向寻求更根本的内服解决方案，体现为首次点击嘎日迪百癣夏塔热胶囊 <|prod_begin|>；最终，通过在短期内再次点击同一药品 <|prod_begin|>，完成了从初步了解到锁定特定药品的决策过程。这一系列行为清晰地勾勒出他针对皮肤问题的诊疗思路演变，未来他有较大概率会购买此类药品或继续关注相关的健康内容。 +{"logic_chain": {"name": "从泛化皮肤问题到聚焦特定药品成分的诊疗收敛", "events": [{"date": "2025-11-05", "action": "[商品-点击] <|prod_begin|>", "logic": "初始触发：用户开始关注皮肤问题，点击了针对“皮炎湿疹、脱屑红斑”的通用型外用敷料，表现出对皮肤症状的初步认知和解决意图。"}, {"date": "2025-11-27", "action": "[商品-点击] <|prod_begin|>", "logic": "认知聚焦与方案转换：用户的兴趣从外用敷料转向了具体的口服药物“百癣夏塔热胶囊”，其适应症（各类癣、皮炎、痤疮）更广谱，表明用户可能在寻求更根本的内服解决方案，实现了从“外用”到“内服”的认知跃迁。"}, {"date": "2025-12-03", "action": "[商品-点击] <|prod_begin|>", "logic": "决策强化与验证：在首次点击该药品后的一周内，用户再次点击同一药品。这种重复行为极大地增强了逻辑链的置信度，表明用户正在对该特定药品进行持续的评估和考虑，而非随机浏览，完成了从“发现”到“锁定”的决策收敛过程。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接到的任务是分析一位用户在女性塑身与保暖下装方面的消费决策过程，需要从她的一系列交互行为中，梳理出一条从初步的功能诉求到最终品类聚焦的完整逻辑链。 + +在分析初期，我注意到这位用户的行为历史相当丰富，涵盖了从服饰、美妆、汽车用品到生鲜食品等多个领域，展现出多元化的生活需求。然而，在2026年2月初，我观察到了一系列高度集中的行为，这正是我此次任务的核心。 + +我的分析起点是2026年2月2日。在这一天，我发现用户点击了一款人鱼塑身衣 <|prod_begin|>。这件商品带有非常明确的功能指向——“收腰显瘦”。我认为，这标志着用户对身材塑形需求的初步显现，她开始主动探索能够优化身体曲线的功能性服装。这不仅仅是一次随意的浏览，而是她决策旅程的开端，明确了她最核心的诉求。 + +紧接着，在同一天，用户的注意力发生了有趣的迁移。她点击了一款“光腿神器” <|prod_begin|>。这个行为非常关键。光腿神器这个品类本身就是一种复合型产品，它巧妙地将保暖（加绒）、美观（仿真肤色）以及一定程度的塑形功能结合在一起。从单一、强功能的塑身衣，到这种兼顾多种场景和需求的产品，我推断用户的需求正在演化。她可能意识到，单纯的塑身衣在日常穿搭中存在局限，而她需要的是一种既能满足塑形效果，又能适应秋冬季节保暖和美观需求的综合性解决方案。 + +两天后的2月4日，用户的探索进一步深化。她点击了一款鲨鱼裤 <|prod_begin|>。与之前点击的商品相比，这款产品的描述更为具体，不仅强调了“高腰收腹提臀”的塑形功能，还特别标注了“羊毛、蚕丝”等更具体的保暖材质。这表明用户已经不满足于品类的泛泛了解，而是进入了更细致的横向对比阶段。她正在比较不同品类（如光腿神器、鲨鱼裤）在材质、保暖效果和塑形细节上的差异，试图找到最符合自己期望的产品。这个行为节点展现了她认知深化的过程，从“我需要塑形”发展到“我需要哪种材质、哪种版型的塑形保暖裤”。 + +最终，在2026年2月5日，用户的探索迎来了决策闭环。她购买了一款光腿神器 <|prod_begin|>。这款产品的描述精准地回应了她此前的所有探索点，明确标注了“保暖”、“高腰收腹”和“自然裸感”。经过对塑身衣、不同材质的鲨鱼裤和光腿神器的综合比较后，她最终选择了光腿神器作为满足其当前需求的最佳方案。这次购买行为，是她从一个宽泛的功能诉求出发，通过对不同品类的发散性探索和对比，最终收敛到一个具体品类并完成消费决策的完整体现。 + +综合来看，我认为用户的决策路径清晰地展现了一个从功能诉求到品类聚焦的演进过程。她最初由一个明确的塑形需求驱动，点击了人鱼塑身衣 <|prod_begin|>，随后将需求扩展至兼顾保暖与美观，开始关注光腿神器 <|prod_begin|>。接着，她通过点击材质和功能更细化的鲨鱼裤 <|prod_begin|> 进行深入对比，最终在充分权衡后，购买了功能全面的光腿神器 <|prod_begin|>，完成了整个决策。基于此，未来向她推荐兼具塑形、保暖和时尚设计感的下装，有较大概率被吸引。 +{"logic_chain": {"name": "从功能诉求到品类聚焦：女性塑身与保暖下装的决策过程", "events": [{"date": "2026-02-02", "action": "[商品-点击] <|prod_begin|>", "logic": "初始探索（塑形）：点击强功能性的塑身衣，明确了对“收腰显瘦”的核心诉求。"}, {"date": "2026-02-02", "action": "[商品-点击] <|prod_begin|>", "logic": "需求融合探索：开始关注“光腿神器”这一品类，该品类融合了“保暖”（加绒）、“美观”（仿真肤色）和一定的“塑形”功能，是对单一塑身衣诉求的补充和演化。"}, {"date": "2026-02-04", "action": "[商品-点击] <|prod_begin|>", "logic": "材质与功能对比：进一步点击了包含更具体材质（羊毛、蚕丝）和功能描述（高腰收腹提臀）的“鲨鱼裤”，在不同品类间进行横向对比，深化对产品细节的认知。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策收敛与闭环：在对比了多种塑形、保暖下装后，最终决策购买了一款明确标注“保暖”、“高腰收腹”的“光腿神器”，完成了从多品类发散探索到单一品类精准购买的闭环。"}]}}<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-08 01:09:10,965 >> 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] 2026-07-08 01:09:10,965 >> 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|2026-07-08 01:09:10] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-08 01:09:11] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-08 01:09:12,314 >> 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] 2026-07-08 01:09:12,314 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-08 01:09:12,380 >> 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] 2026-07-08 01:09:13,700 >> 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] 2026-07-08 01:09:14,298 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-08 01:09:14] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-08 01:09:14] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-08 01:09:14] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-08 01:09:14] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-08 01:09:14] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-08 01:09:14,338 >> 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] 2026-07-08 01:09:14,445 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-08 01:09:14,445 >> Num examples = 3,467 +[INFO|trainer.py:1471] 2026-07-08 01:09:14,445 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-08 01:09:14,445 >> Num update steps per epoch = 867 +[INFO|trainer.py:1473] 2026-07-08 01:09:14,445 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-08 01:09:14,445 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-08 01:09:14,445 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-08 01:09:14,445 >> Total optimization steps = 867 +[INFO|trainer.py:1479] 2026-07-08 01:09:14,445 >> Number of trainable parameters = 801,433,600 + 0%| | 0/867 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 867/867 [2:06:27<00:00, 7.97s/it] 100%|██████████| 867/867 [2:06:27<00:00, 8.75s/it] +[INFO|trainer.py:3815] 2026-07-08 03:15:41,762 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6 +[INFO|configuration_utils.py:533] 2026-07-08 03:15:41,763 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/config.json +[INFO|configuration_utils.py:816] 2026-07-08 03:15:41,763 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/generation_config.json + +{'loss': '1.496', 'grad_norm': '2.672', 'learning_rate': '6.884e-06', 'epoch': '0.5423'} +{'loss': '1.449', 'grad_norm': '2.641', 'learning_rate': '6.744e-06', 'epoch': '0.548'} +{'loss': '1.392', 'grad_norm': '2.625', 'learning_rate': '6.605e-06', 'epoch': '0.5538'} +{'loss': '1.409', 'grad_norm': '2.797', 'learning_rate': '6.465e-06', 'epoch': 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model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-08 03:15:42,731 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-08 03:15:42,752 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 391467031GF + train_loss = 1.4888 + train_runtime = 2:06:27.31 + train_samples_per_second = 0.457 + train_steps_per_second = 0.114 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v15_user_logic_json_lr15e6/training_loss.png +[WARNING|2026-07-08 03:15:42] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-08 03:15:42] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-08 03:15:42,852 >> 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..b5ae52c --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 4.2033452439454925e+17, + "train_loss": 1.4887716360433154, + "train_runtime": 7587.3157, + "train_samples_per_second": 0.457, + "train_steps_per_second": 0.114 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..58f6d6a --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,174 @@ +{"current_steps": 5, "total_steps": 867, "loss": 2.812992477416992, "lr": 2.222222222222222e-06, "epoch": 0.005768676088837612, "percentage": 0.58, "elapsed_time": "0:00:44", "remaining_time": "2:06:30"} +{"current_steps": 10, "total_steps": 867, "loss": 2.6157678604125976, "lr": 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