From 83b9eccdfc5bee0ce33162640d22eb3cc98c450f Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 18 Jul 2026 04:58:12 +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/v18_v12_unify_replay_lr5e6_ep05 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 + ...51788.ubuntu-System-Product-Name.3936671.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2163 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 57 + trainer_state.json | 435 ++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 51861 bytes 16 files changed, 2957 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_03-16-28_ubuntu-System-Product-Name/events.out.tfevents.1783451788.ubuntu-System-Product-Name.3936671.0 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 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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..891ca6f --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v12_user_cot_focus_lr15e6 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v18_v12_unify_replay_lr5e6_ep05 + results: [] +--- + + + +# v18_v12_unify_replay_lr5e6_ep05 + +This model is a fine-tuned version of [lldois/v12_user_cot_focus_lr15e6](https://huggingface.co/lldois/v12_user_cot_focus_lr15e6) on the exp_v12_unify_replay dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-06 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607184 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.02 +- num_epochs: 0.5 + +### 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..432f859 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.5011027790030878, + "total_flos": 1.3705181662943232e+17, + "train_loss": 1.5080752087311007, + "train_runtime": 2339.7979, + "train_samples_per_second": 0.484, + "train_steps_per_second": 0.121 +} \ 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..4db58e2 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed0737abc4b2024a79654c3934d4ec837678f7e030a2350d90776e27401c28cc +size 1602902832 diff --git a/runs/Jul08_03-16-28_ubuntu-System-Product-Name/events.out.tfevents.1783451788.ubuntu-System-Product-Name.3936671.0 b/runs/Jul08_03-16-28_ubuntu-System-Product-Name/events.out.tfevents.1783451788.ubuntu-System-Product-Name.3936671.0 new file mode 100644 index 0000000..597fe82 --- /dev/null +++ b/runs/Jul08_03-16-28_ubuntu-System-Product-Name/events.out.tfevents.1783451788.ubuntu-System-Product-Name.3936671.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46606d88e6fdd766d9d8f6aaff4014e89a5578576ed72504ed379e72ed0151c9 +size 18015 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..35d445f --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": true, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..1f135fb --- /dev/null +++ b/train.log @@ -0,0 +1,2163 @@ +/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 03:15:59] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:769] 2026-07-08 03:15:59,755 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/config.json +[INFO|configuration_utils.py:847] 2026-07-08 03:15:59,757 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-08 03:16:00,429 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/config.json +[INFO|configuration_utils.py:847] 2026-07-08 03:16:00,430 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:769] 2026-07-08 03:16:00,430 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/config.json +[INFO|configuration_utils.py:847] 2026-07-08 03:16:00,431 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-08 03:16:01] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v12_unify_replay.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: 13093 examples [00:00, 119821.31 examples/s] Generating train split: 28928 examples [00:00, 135703.19 examples/s] Generating train split: 44562 examples [00:00, 141230.54 examples/s] Generating train split: 46277 examples [00:00, 138609.25 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/46277 [00:00user +【用户交互历史】： +【2025-07-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> 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<|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:58 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + 23:28 [广告-深度转化] <|ad_begin|> + 23:28 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-22】 + 10:47 [视频-收藏/点赞/长播] <|video_begin|> + 10:51 [视频-评论/长播] <|video_begin|> + 23:56 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:54 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-24】 + 02:58 [视频-关注/长播] <|video_begin|> + 09:12 [视频-评论/长播] <|video_begin|> + 09:23 [视频-评论/长播] <|video_begin|> + 15:07 [视频-评论/长播] <|video_begin|> +【2026-01-25】 + 23:35 [广告-深度转化] <|ad_begin|> + 23:35 [广告-点击] <|ad_begin|> +【2026-01-27】 + 21:51 [视频-评论/长播] <|video_begin|> + 21:56 [视频-评论/长播] <|video_begin|> + 22:20 [视频-评论/长播] <|video_begin|> + 22:51 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-29】 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[视频-评论/点赞/长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 14:41 [视频-长播] <|video_begin|> + 15:08 [视频-长播] <|video_begin|> + 15:08 [视频-长播] <|video_begin|> + 15:08 [视频-长播] <|video_begin|> + 15:19 [视频-长播] <|video_begin|> + 15:32 [视频-长播] <|video_begin|> + 15:36 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> + 16:27 [视频-长播] <|video_begin|> + 16:27 [视频-长播] <|video_begin|> + 16:27 [视频-长播] <|video_begin|> + 16:36 [视频-点赞/长播] <|video_begin|> + 16:43 [视频-点赞/长播] <|video_begin|> + 16:43 [视频-点赞/长播] <|video_begin|> + 16:43 [视频-点赞/长播] <|video_begin|> + 16:43 [视频-点赞/长播] <|video_begin|> + 16:43 [视频-长播] <|video_begin|> + 16:48 [视频-点赞/长播] <|video_begin|> + 16:48 [视频-长播] <|video_begin|> + 16:53 [视频-点赞/长播] <|video_begin|> + 16:54 [视频-长播] <|video_begin|> + 16:56 [视频-长播] <|video_begin|> + 16:59 [视频-点赞/长播] <|video_begin|> + 17:00 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + 00:10 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:23 [视频-点赞/长播] <|video_begin|> + 00:24 [视频-点赞/长播] <|video_begin|> + 00:24 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 06:41 [视频-点赞/长播] <|video_begin|> + 06:42 [视频-点赞/长播] <|video_begin|> + 06:43 [视频-点赞/长播] <|video_begin|> + 07:25 [视频-点赞/长播] <|video_begin|> + 07:25 [视频-长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-点赞/长播] <|video_begin|> + 16:08 [视频-点赞/长播] <|video_begin|> + 16:08 [视频-长播] <|video_begin|> + 16:08 [视频-长播] <|video_begin|> + 16:19 [视频-点赞/长播] <|video_begin|> + 16:24 [视频-点赞/长播] <|video_begin|> + 16:24 [视频-长播] <|video_begin|> + 16:24 [视频-长播] <|video_begin|> + 16:24 [视频-长播] <|video_begin|> + 16:32 [视频-点赞/长播] <|video_begin|> + 16:32 [视频-长播] <|video_begin|> + 16:37 [视频-点赞/长播] <|video_begin|> + 16:37 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 16:52 [视频-长播] <|video_begin|> + 16:54 [视频-长播] <|video_begin|> + 16:58 [视频-点赞/长播] <|video_begin|> + 16:59 [视频-点赞/长播] <|video_begin|> + 16:59 [视频-长播] <|video_begin|> + 17:01 [视频-点赞/长播] <|video_begin|> + 17:01 [视频-点赞/长播] <|video_begin|> + 17:05 [视频-点赞/长播] <|video_begin|> + 23:52 [视频-点赞/长播] <|video_begin|> + 23:52 [视频-点赞/长播] <|video_begin|> + 23:56 [视频-点赞/长播] <|video_begin|> + 23:56 [视频-点赞/长播] <|video_begin|> + 23:56 [视频-点赞/长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:01 [视频-点赞/长播] <|video_begin|> + 00:01 [视频-评论/点赞/长播] <|video_begin|> + 00:01 [视频-评论/点赞/长播] <|video_begin|> + 00:05 [视频-点赞/长播] <|video_begin|> + 00:05 [视频-长播] <|video_begin|> + 00:09 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:44 [广告-点击] <|ad_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 11:20 [视频-长播] <|video_begin|> + 12:33 [视频-点赞/长播] <|video_begin|> + 12:33 [视频-点赞/长播] <|video_begin|> + 12:33 [视频-评论/点赞/长播] <|video_begin|> + 13:19 [视频-点赞/长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 与 交互 A 必须在同一生活场景、主题类目下具有极强的需求补完关系。 +2.兴趣因果递进：交互 B 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下，交互 B 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请围绕以下主题提取行为逻辑链： +主题：从被动接收信息到主动实践的手工兴趣唤醒 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收信息到主动实践的手工兴趣唤醒", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这位用户的行为。这项任务要求我基于用户的交互历史，挖掘出一条从被动接收信息到主动实践的手工兴趣唤醒的行为链。我需要重点关注行为之间的时间顺序和逻辑关联，确保后续行为是由前序行为引发的。 + +我首先对用户的整体行为进行了梳理。从2025年7月到2026年初，我注意到这位用户的消费行为非常丰富，涵盖了食品零食、服饰鞋包、美妆护肤、家居日用以及一些健身器材等多个品类。这表明她是一位乐于尝试和消费的年轻女性。在内容消费方面，尤其是在2026年1月底到2月初，她密集地观看了大量视频，内容五花八门，包括搞笑短剧、明星八卦、社会新闻、情感故事、影视剪辑和生活技巧等，显示出她兴趣广泛，但似乎没有特别聚焦的领域。 + +我的分析重点放在了寻找一个从“看”到“做”的转变点上。在浏览2026年2月3日的行为时，我发现了一个非常清晰的信号。在当天上午08:20，用户观看并评论了一个展示手工编织围巾的视频 <|video_begin|>。这个视频强调了手工制品所蕴含的“情感价值”和“人情味”，这是一种情感层面的触动，而不仅仅是展示商品。我推测，这种情感化的内容很可能激发了用户自己动手制作的念头。 + +紧接着，我发现用户的行为立刻印证了我的推测。就在观看完视频之后，同一天内，她开始密集地点击与“手工编织”相关的商品。例如，她点击了标题中包含“手工长绒毛围巾diy自制编织心意礼物”的商品 <|prod_begin|>，以及多款以“蜡笔小新”为主题的DIY围巾材料包。这些点击行为中的关键词，如“diy”、“手工编织”、“心意礼物”，与她刚刚观看的视频内容高度契合。这表明她的兴趣已经从被动的观看，迅速转化为主动的、有明确目的的商品调研。她不再只是欣赏成品，而是在寻找能够让她亲身实践的工具和材料。 + +最终，在经过一番浏览和比较之后，用户在当天就完成了购买决策。她购买了一款名为“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包”的商品 <|prod_begin|>。这个购买行为是整个兴趣链条的关键闭环，标志着她完成了从被动的信息接收者到主动的实践者的身份转变。整个过程从看到视频激发兴趣，到搜索和比较DIY材料，再到最终下单购买，逻辑清晰，时间紧凑，构成了一条完整的兴趣唤醒与实践路径。 + +综合以上分析，我认为用户的行为清晰地展示了从被动接收信息到主动实践的手工兴趣唤醒过程。这个过程始于观看一个强调情感价值的手工编织视频 <|video_begin|>，这激发了她的制作兴趣；随后，她立即开始浏览具体的DIY围巾材料包商品，例如点击了“手工长绒毛围巾diy自制编织心意礼物” <|prod_begin|>，将模糊的兴趣转化为明确的行动意图；最终，通过购买“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包” <|prod_begin|>，完成了从认知到行为的转化。因此，未来向她推荐更多DIY手工制作类的内容或商品，有较大概率被吸引。 +{"logic_chain": {"name": "从被动接收信息到主动实践的手工兴趣唤醒", "events": [{"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣触发：被动观看到手工编织围巾的视频，内容强调“情感价值”和“人情味”，可能激发了用户的制作兴趣。"}, {"date": "2026-02-03", "action": "[商品-点击] <|prod_begin|>", "logic": "意图确认与调研：观看视频后，用户立即开始浏览具体的“DIY材料包”商品，关键词“手工”、“diy自制”、“心意礼物”与视频内容高度关联，表明兴趣已转化为具体行动意图。"}, {"date": "2026-02-03", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在浏览了多种DIY围巾材料包后，用户最终完成购买，实现了从“被动观看”到“主动实践”的认知与行为跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-17】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-29】 + --:-- [直播-关注] <|living_begin|> +【2025-04-02】 + --:-- [直播-关注] <|living_begin|> +【2025-04-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-10】 + --:-- [直播-关注] <|living_begin|> +【2025-04-24】 + --:-- [直播-关注] <|living_begin|> +【2025-05-02】 + --:-- [直播-关注] <|living_begin|> +【2025-05-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [直播-关注] <|living_begin|> +【2025-06-12】 + --:-- [直播-关注] <|living_begin|> +【2025-06-15】 + --:-- [直播-关注] <|living_begin|> +【2025-06-21】 + --:-- [直播-关注] <|living_begin|> +【2025-06-25】 + --:-- [直播-关注] <|living_begin|> +【2025-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-25】 + --:-- [直播-关注] <|living_begin|> +【2025-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-19】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [直播-关注] <|living_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> +【2026-01-19】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-21】 + 19:18 [视频-评论/长播] <|video_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> +【2026-01-23】 + 04:51 [视频-关注/长播] <|video_begin|> + 05:40 [视频-关注/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + 06:44 [视频-关注/长播] <|video_begin|> + 06:58 [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|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:15 [视频-点赞/长播] <|video_begin|> + 19:04 [视频-浏览] <|video_begin|> +【2026-01-26】 + 00:03 [广告-深度转化] <|ad_begin|> + 00:24 [广告-深度转化] <|ad_begin|> + 00:31 [广告-深度转化] <|ad_begin|> + 00:32 [广告-深度转化] <|ad_begin|> + 05:45 [广告-深度转化] <|ad_begin|> + 07:49 [广告-深度转化] <|ad_begin|> + 07:50 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:14 [广告-深度转化] <|ad_begin|> + 08:31 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:47 [广告-深度转化] <|ad_begin|> + 08:56 [广告-深度转化] <|ad_begin|> + 08:56 [广告-深度转化] <|ad_begin|> + 09:04 [广告-深度转化] <|ad_begin|> + 09:05 [广告-深度转化] <|ad_begin|> + 11:08 [广告-深度转化] <|ad_begin|> +【2026-01-27】 + 14:08 [广告-深度转化] <|ad_begin|> + 14:20 [广告-深度转化] <|ad_begin|> +【2026-01-28】 + 02:51 [视频-点赞/长播] <|video_begin|> + 10:12 [广告-深度转化] <|ad_begin|> + 10:14 [广告-深度转化] <|ad_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 10:31 [广告-深度转化] <|ad_begin|> + 10:34 [广告-深度转化] <|ad_begin|> + 12:49 [广告-深度转化] <|ad_begin|> + 22:59 [广告-深度转化] <|ad_begin|> +【2026-01-29】 + 13:14 [广告-深度转化] <|ad_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 13:20 [广告-深度转化] <|ad_begin|> + 15:41 [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|> +【2026-01-30】 + 01:33 [广告-深度转化] <|ad_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 08:23 [广告-深度转化] <|ad_begin|> + 08:27 [广告-深度转化] <|ad_begin|> + 09:32 [广告-深度转化] <|ad_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 11:02 [广告-深度转化] <|ad_begin|> + 11:02 [广告-点击] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> + 11:05 [广告-点击] <|ad_begin|> + 11:05 [广告-点击] <|ad_begin|> + 11:08 [广告-点击] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 14:49 [广告-点击] <|ad_begin|> + 15:20 [广告-点击] <|ad_begin|> + 15:25 [广告-点击] <|ad_begin|> + 22:15 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:07 [广告-深度转化] <|ad_begin|> + 23:07 [广告-点击] <|ad_begin|> +【2026-01-31】 + 00:22 [广告-深度转化] <|ad_begin|> + 00:22 [广告-点击] <|ad_begin|> + 00:32 [广告-深度转化] <|ad_begin|> + 00:32 [广告-点击] <|ad_begin|> + 03:30 [广告-点击] <|ad_begin|> + 05:25 [广告-点击] <|ad_begin|> + 08:40 [视频-浏览] <|video_begin|> + 09:51 [视频-浏览] <|video_begin|> + 10:40 [广告-深度转化] <|ad_begin|> + 10:40 [广告-点击] <|ad_begin|> + 12:42 [广告-深度转化] <|ad_begin|> + 12:42 [广告-点击] <|ad_begin|> + 12:46 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:48 [广告-深度转化] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:49 [广告-深度转化] <|ad_begin|> + 12:49 [广告-点击] <|ad_begin|> + 12:50 [广告-深度转化] <|ad_begin|> + 12:50 [广告-点击] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + 13:00 [广告-深度转化] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> + 13:01 [广告-点击] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> + 13:47 [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:28 [广告-深度转化] <|ad_begin|> + 23:28 [广告-点击] <|ad_begin|> + 23:29 [广告-点击] <|ad_begin|> +【2026-02-01】 + 00:13 [广告-深度转化] <|ad_begin|> + 00:13 [广告-点击] <|ad_begin|> + 00:14 [广告-深度转化] <|ad_begin|> + 00:14 [广告-点击] <|ad_begin|> + 09:10 [广告-深度转化] <|ad_begin|> + 09:10 [广告-点击] <|ad_begin|> + 09:50 [广告-深度转化] <|ad_begin|> + 09:50 [广告-点击] <|ad_begin|> + 10:35 [广告-深度转化] <|ad_begin|> + 10:35 [广告-点击] <|ad_begin|> + 10:42 [广告-深度转化] <|ad_begin|> + 10:42 [广告-点击] <|ad_begin|> + 11:21 [广告-点击] <|ad_begin|> + 11:22 [广告-点击] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 19:54 [视频-浏览] <|video_begin|> + 21:36 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-02】 + 03:55 [视频-点赞/长播] <|video_begin|> + 05:14 [广告-点击] <|ad_begin|> + 05:42 [广告-深度转化] <|ad_begin|> + 05:42 [广告-点击] <|ad_begin|> + 07:03 [视频-收藏/长播] <|video_begin|> + 09:28 [广告-点击] <|ad_begin|> + 14:20 [广告-点击] <|ad_begin|> + 14:20 [广告-点击] <|ad_begin|> + 14:21 [广告-深度转化] <|ad_begin|> + 14:21 [广告-点击] <|ad_begin|> + 14:21 [广告-深度转化] <|ad_begin|> + 14:21 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 14:25 [广告-点击] <|ad_begin|> + 21:47 [视频-关注/转发/点赞/长播] <|video_begin|> + 22:52 [广告-深度转化] <|ad_begin|> + 22:52 [广告-点击] <|ad_begin|> + 23:48 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 15:52 [广告-深度转化] <|ad_begin|> + 15:52 [广告-点击] <|ad_begin|> + 18:02 [广告-深度转化] <|ad_begin|> + 18:02 [广告-点击] <|ad_begin|> + 18:08 [广告-深度转化] <|ad_begin|> + 18:08 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-04】 + 00:05 [广告-深度转化] <|ad_begin|> + 00:05 [广告-点击] <|ad_begin|> + 00:05 [广告-深度转化] <|ad_begin|> + 00:05 [广告-点击] <|ad_begin|> + 10:20 [广告-点击] <|ad_begin|> + 10:58 [广告-深度转化] <|ad_begin|> + 10:58 [广告-点击] <|ad_begin|> + 13:52 [视频-长播] <|video_begin|> + 16:04 [广告-深度转化] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:48 [广告-点击] <|ad_begin|> + 21:58 [视频-长播] <|video_begin|> + 21:58 [视频-长播] <|video_begin|> + 22:10 [视频-长播] <|video_begin|> + 22:10 [视频-长播] <|video_begin|> + 22:10 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:17 [视频-长播] <|video_begin|> + 22:17 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:23 [视频-长播] <|video_begin|> + 22:23 [视频-长播] <|video_begin|> + 22:23 [视频-长播] <|video_begin|> + 22:23 [视频-长播] <|video_begin|> + 22:23 [视频-长播] <|video_begin|> + 22:26 [视频-长播] <|video_begin|> + 22:26 [视频-长播] <|video_begin|> + 22:32 [视频-长播] <|video_begin|> + 22:32 [视频-长播] <|video_begin|> + 22:32 [视频-长播] <|video_begin|> + 22:32 [视频-长播] <|video_begin|> + 22:32 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:08 [广告-深度转化] <|ad_begin|> + 00:08 [广告-点击] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 06:46 [广告-点击] <|ad_begin|> + 11:33 [视频-长播] <|video_begin|> + 11:33 [视频-长播] <|video_begin|> + 11:33 [视频-长播] <|video_begin|> + 12:00 [视频-长播] <|video_begin|> + 12:00 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 14:35 [广告-点击] <|ad_begin|> + 14:36 [广告-点击] <|ad_begin|> + 14:36 [广告-点击] <|ad_begin|> + 14:36 [广告-点击] <|ad_begin|> + 15:32 [广告-点击] <|ad_begin|> + 15:42 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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）： +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 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。 +请针对下列主题提取行为逻辑链： +主题：从泛化需求到精准定位的染发产品探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化需求到精准定位的染发产品探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化需求到精准定位的染发产品探索", "events": [{"date": "2025-10-10", "action": "[商品-购买] <|prod_begin|>", "logic": "初始行为：购买通用型染发产品，满足基础的染发需求。"}, {"date": "2026-01-04", "action": "[商品-购买] <|prod_begin|>", "logic": "需求聚焦与路径修正：意识到常规染发无法应对即时的白发问题，转向购买“遮盖白发神器”这种功能更具体、使用更便捷的“美发棒”产品，体现了对“补染”这一细分场景的认知增量。"}, {"date": "2026-01-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策优化与品质升级：在尝试了便捷性产品后，回归到染发膏品类，但选择了带有“二代升级”、“单剂”、“护染”等关键词的产品，表明用户在追求便捷的同时，开始关注产品的升级换代和养护功能，需求从“遮盖”深化为“便捷且护发”的更高阶需求。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-06】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-05】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-08】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-06-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-21】 + --:-- [直播-打赏] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-09】 + --:-- [直播-关注] <|living_begin|> +【2025-07-11】 + --:-- [直播-关注] <|living_begin|> +【2025-07-15】 + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [直播-关注] <|living_begin|> +【2025-07-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-24】 + --:-- [直播-关注] <|living_begin|> +【2025-07-29】 + --:-- [直播-关注] <|living_begin|> +【2025-07-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-09】 + --:-- [直播-关注] <|living_begin|> +【2025-08-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-22】 + --:-- [直播-关注] <|living_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-08-28】 + --:-- [直播-关注] <|living_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-03】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-22】 + --:-- [直播-关注] <|living_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> + 02:08 [广告-深度转化] <|ad_begin|> +【2025-12-13】 + 21:37 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + 07:48 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + 06:22 [广告-深度转化] <|ad_begin|> + 09:03 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + 06:14 [广告-深度转化] <|ad_begin|> + 07:47 [广告-深度转化] <|ad_begin|> + 13:04 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + 22:47 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + 03:35 [广告-深度转化] <|ad_begin|> + 08:09 [广告-深度转化] <|ad_begin|> + 09:42 [广告-深度转化] <|ad_begin|> + 10:50 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> + 07:13 [广告-深度转化] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> + 19:18 [广告-点击] <|ad_begin|> + 19:23 [广告-深度转化] <|ad_begin|> + 19:23 [广告-点击] <|ad_begin|> + 21:14 [广告-深度转化] <|ad_begin|> + 21:14 [广告-点击] <|ad_begin|> +【2025-12-27】 + 01:07 [广告-点击] <|ad_begin|> + 01:28 [广告-深度转化] <|ad_begin|> + 01:28 [广告-点击] <|ad_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-点击] <|ad_begin|> + 23:21 [广告-点击] <|ad_begin|> + 23:40 [广告-深度转化] <|ad_begin|> + 23:40 [广告-点击] <|ad_begin|> +【2025-12-28】 + 13:59 [广告-点击] <|ad_begin|> + 14:06 [广告-点击] <|ad_begin|> + 14:09 [广告-点击] <|ad_begin|> +【2025-12-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 01:00 [广告-深度转化] <|ad_begin|> + 01:00 [广告-点击] <|ad_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 04:59 [广告-点击] <|ad_begin|> + 06:38 [广告-深度转化] <|ad_begin|> + 06:38 [广告-点击] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> +【2025-12-31】 + 17:59 [广告-点击] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + 23:33 [广告-深度转化] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-01-02】 + 00:50 [广告-深度转化] <|ad_begin|> + 00:50 [广告-点击] <|ad_begin|> + 11:36 [广告-点击] <|ad_begin|> + 14:38 [广告-深度转化] <|ad_begin|> + 14:38 [广告-点击] <|ad_begin|> +【2026-01-03】 + 07:14 [广告-点击] <|ad_begin|> +【2026-01-04】 + 04:03 [广告-深度转化] <|ad_begin|> + 04:03 [广告-点击] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 04:52 [广告-点击] <|ad_begin|> + 09:24 [广告-深度转化] <|ad_begin|> + 09:24 [广告-点击] <|ad_begin|> + 09:52 [广告-深度转化] <|ad_begin|> + 09:52 [广告-点击] <|ad_begin|> +【2026-01-05】 + 10:48 [广告-点击] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> + 12:26 [广告-点击] <|ad_begin|> +【2026-01-06】 + 06:52 [广告-点击] <|ad_begin|> + 06:54 [广告-点击] <|ad_begin|> + 12:14 [广告-深度转化] <|ad_begin|> + 12:14 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + 03:49 [广告-深度转化] <|ad_begin|> + 03:49 [广告-点击] <|ad_begin|> + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + 22:11 [广告-深度转化] <|ad_begin|> + 22:11 [广告-点击] <|ad_begin|> +【2026-01-08】 + 03:46 [广告-点击] <|ad_begin|> + 10:21 [广告-深度转化] <|ad_begin|> + 10:21 [广告-点击] <|ad_begin|> + 10:42 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + 03:25 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + 00:38 [广告-点击] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 10:03 [广告-点击] <|ad_begin|> + 16:29 [广告-深度转化] <|ad_begin|> + 16:29 [广告-点击] <|ad_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> +【2026-01-14】 + 21:34 [广告-深度转化] <|ad_begin|> + 21:34 [广告-点击] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-17】 + 01:19 [广告-点击] <|ad_begin|> + 07:49 [广告-点击] <|ad_begin|> + 21:05 [广告-深度转化] <|ad_begin|> + 21:05 [广告-点击] <|ad_begin|> + 22:23 [广告-深度转化] <|ad_begin|> + 22:23 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-购买] <|prod_begin|> + 09:20 [广告-点击] <|ad_begin|> +【2026-01-20】 + 20:46 [广告-点击] <|ad_begin|> + 20:57 [广告-深度转化] <|ad_begin|> + 20:57 [广告-点击] <|ad_begin|> + 21:18 [广告-深度转化] <|ad_begin|> + 21:18 [广告-点击] <|ad_begin|> +【2026-01-21】 + 19:50 [广告-点击] <|ad_begin|> +【2026-01-22】 + 17:49 [视频-收藏/长播] <|video_begin|> + 20:35 [广告-深度转化] <|ad_begin|> + 20:35 [广告-点击] <|ad_begin|> +【2026-01-23】 + 01:17 [广告-点击] <|ad_begin|> + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> +【2026-01-24】 + 06:07 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + 13:36 [视频-关注/长播] <|video_begin|> + 21:21 [广告-深度转化] <|ad_begin|> + 21:21 [广告-点击] <|ad_begin|> +【2026-01-27】 + 00:05 [视频-关注/长播] <|video_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> + 09:53 [广告-深度转化] <|ad_begin|> + 09:53 [广告-点击] <|ad_begin|> + 22:20 [广告-深度转化] <|ad_begin|> + 22:20 [广告-点击] <|ad_begin|> +【2026-01-28】 + 00:59 [视频-评论/点赞/长播] <|video_begin|> + 01:40 [广告-深度转化] <|ad_begin|> + 01:40 [广告-点击] <|ad_begin|> + 21:02 [视频-收藏/长播] <|video_begin|> + 21:22 [广告-深度转化] <|ad_begin|> + 21:22 [广告-点击] <|ad_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 00:49 [广告-点击] <|ad_begin|> + 22:17 [广告-深度转化] <|ad_begin|> + 22:17 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:48 [广告-点击] <|ad_begin|> + 23:23 [广告-深度转化] <|ad_begin|> + 23:23 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:37 [广告-深度转化] <|ad_begin|> + 02:37 [广告-点击] <|ad_begin|> + 07:31 [广告-点击] <|ad_begin|> + 22:06 [广告-深度转化] <|ad_begin|> + 22:06 [广告-点击] <|ad_begin|> + 22:37 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + 00:30 [视频-长播] <|video_begin|> + 00:33 [视频-长播] <|video_begin|> + 00:33 [视频-长播] <|video_begin|> + 00:33 [视频-长播] <|video_begin|> + 00:51 [视频-长播] <|video_begin|> + 01:02 [视频-长播] <|video_begin|> + 02:18 [视频-长播] <|video_begin|> + 02:18 [视频-长播] <|video_begin|> + 02:23 [视频-长播] <|video_begin|> + 02:26 [视频-长播] <|video_begin|> + 02:26 [视频-长播] <|video_begin|> + 02:26 [视频-长播] <|video_begin|> + 02:26 [视频-长播] <|video_begin|> + 02:40 [视频-长播] <|video_begin|> + 02:40 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 09:32 [视频-长播] <|video_begin|> + 09:32 [视频-长播] <|video_begin|> + 09:32 [视频-转发/长播] <|video_begin|> + 09:43 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 02:27 [视频-长播] <|video_begin|> + 02:27 [视频-长播] <|video_begin|> + 02:36 [视频-长播] <|video_begin|> + 02:36 [视频-关注/长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:46 [视频-长播] <|video_begin|> + 02:46 [视频-长播] <|video_begin|> + 06:46 [视频-长播] <|video_begin|> + 06:46 [视频-长播] <|video_begin|> + 06:51 [视频-长播] <|video_begin|> + 06:51 [视频-长播] <|video_begin|> + 06:52 [视频-长播] <|video_begin|> + 06:52 [视频-长播] <|video_begin|> + 06:52 [视频-长播] <|video_begin|> + 06:57 [视频-长播] <|video_begin|> + 06:57 [视频-长播] <|video_begin|> + 06:57 [视频-长播] <|video_begin|> + 07:09 [视频-长播] <|video_begin|> + 07:09 [视频-长播] <|video_begin|> + 07:09 [视频-长播] <|video_begin|> + 20:16 [视频-长播] <|video_begin|> +【2026-02-05】 + 01:11 [视频-长播] <|video_begin|> + 01:20 [视频-长播] <|video_begin|> + 01:20 [视频-长播] <|video_begin|> + 01:20 [视频-长播] <|video_begin|> + 08:16 [视频-长播] <|video_begin|> + 08:16 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 +核心抽取逻辑（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。 +请针对以下主题提取行为逻辑链： +主题：从娱乐消遣到寻求专业法律咨询的认知跃迁 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从娱乐消遣到寻求专业法律咨询的认知跃迁", + "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 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从娱乐消遣到寻求专业法律咨询的认知跃迁","events":[{"date":"2025-12-22","action":"[广告-深度转化] <|ad_begin|>","logic":"问题域接触：通过观看大量关于“出轨”、“情感纠葛”、“家庭矛盾”等主题的短剧广告，被动地接触并沉浸在相关问题情境中。"},{"date":"2026-01-06","action":"[直播-首次打赏] <|living_begin|>","logic":"认知跃迁与路径修正：用户的兴趣点从虚构的剧情消费，转向了现实中的专业解决方案。主动对提供“刑案咨询”和“实务讲解”的直播主进行打赏，表明其需求从娱乐消遣升级为获取专业法律知识，是典型的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-04】 + --:-- [直播-关注/打赏] <|living_begin|> +【2025-01-12】 + --:-- [直播-关注] <|living_begin|> +【2025-02-09】 + --:-- [直播-关注] <|living_begin|> +【2025-03-28】 + --:-- [直播-关注] <|living_begin|> +【2025-04-05】 + --:-- [直播-打赏] <|living_begin|> +【2025-04-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-28】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-10】 + --:-- [直播-关注] <|living_begin|> +【2025-05-31】 + --:-- [直播-关注] <|living_begin|> +【2025-06-02】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注] <|living_begin|> +【2025-07-05】 + --:-- [直播-关注] <|living_begin|> +【2025-08-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-08-29】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-22】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-29】 + --:-- [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|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-10】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + 22:39 [广告-深度转化] <|ad_begin|> + 23:18 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 02:46 [广告-深度转化] <|ad_begin|> + 03:09 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 00:24 [广告-深度转化] <|ad_begin|> + 01:11 [广告-深度转化] <|ad_begin|> + 05:46 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + 03:00 [广告-深度转化] <|ad_begin|> + 04:47 [广告-深度转化] <|ad_begin|> + 04:47 [广告-点击] <|ad_begin|> + 11:06 [广告-深度转化] <|ad_begin|> + 11:06 [广告-点击] <|ad_begin|> + 13:19 [广告-深度转化] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:44 [广告-深度转化] <|ad_begin|> + 13:44 [广告-点击] <|ad_begin|> + 14:09 [广告-点击] <|ad_begin|> + 14:23 [广告-深度转化] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 15:45 [广告-深度转化] <|ad_begin|> + 15:45 [广告-点击] <|ad_begin|> + 22:08 [广告-深度转化] <|ad_begin|> + 22:08 [广告-点击] <|ad_begin|> + 22:28 [广告-深度转化] <|ad_begin|> + 22:28 [广告-点击] <|ad_begin|> +【2026-01-19】 + 01:27 [广告-深度转化] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 02:07 [广告-深度转化] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:26 [广告-深度转化] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 04:53 [广告-深度转化] <|ad_begin|> + 04:53 [广告-点击] <|ad_begin|> + 05:43 [广告-深度转化] <|ad_begin|> + 05:43 [广告-点击] <|ad_begin|> + 07:11 [广告-点击] <|ad_begin|> + 10:41 [广告-点击] <|ad_begin|> + 11:20 [广告-深度转化] <|ad_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:37 [广告-点击] <|ad_begin|> + 23:11 [广告-深度转化] <|ad_begin|> + 23:11 [广告-点击] <|ad_begin|> + 23:25 [广告-深度转化] <|ad_begin|> + 23:25 [广告-点击] <|ad_begin|> +【2026-01-20】 + 02:26 [广告-深度转化] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 02:57 [广告-点击] <|ad_begin|> + 03:17 [广告-深度转化] <|ad_begin|> + 03:17 [广告-点击] <|ad_begin|> + 05:00 [广告-深度转化] <|ad_begin|> + 05:00 [广告-点击] <|ad_begin|> + 05:53 [广告-深度转化] <|ad_begin|> + 05:53 [广告-点击] <|ad_begin|> + 09:24 [广告-深度转化] <|ad_begin|> + 09:24 [广告-点击] <|ad_begin|> + 13:48 [广告-深度转化] <|ad_begin|> + 13:48 [广告-点击] <|ad_begin|> +【2026-01-21】 + 01:18 [广告-深度转化] <|ad_begin|> + 01:18 [广告-点击] <|ad_begin|> + 05:02 [广告-深度转化] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + 14:04 [广告-点击] <|ad_begin|> +【2026-01-22】 + 02:02 [广告-深度转化] <|ad_begin|> + 02:02 [广告-点击] <|ad_begin|> + 07:41 [广告-深度转化] <|ad_begin|> + 07:41 [广告-点击] <|ad_begin|> + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> + 22:49 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 01:45 [广告-深度转化] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 07:29 [广告-深度转化] <|ad_begin|> + 07:29 [广告-点击] <|ad_begin|> + 10:01 [广告-点击] <|ad_begin|> + 11:05 [广告-深度转化] <|ad_begin|> + 11:05 [广告-点击] <|ad_begin|> +【2026-01-25】 + 02:22 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:48 [视频-点赞/长播] <|video_begin|> + 14:30 [广告-点击] <|ad_begin|> + 14:31 [视频-点赞/长播] <|video_begin|> + 21:37 [视频-点赞/长播] <|video_begin|> + 22:09 [广告-点击] <|ad_begin|> + 22:29 [广告-点击] <|ad_begin|> +【2026-01-26】 + 00:32 [视频-关注/点赞/长播] <|video_begin|> + 03:13 [广告-深度转化] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 08:27 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-评论/点赞/长播] <|video_begin|> + 11:40 [视频-浏览] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:13 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 00:54 [视频-点赞/长播] <|video_begin|> + 01:28 [视频-点赞/长播] <|video_begin|> + 04:40 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:10 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:28 [广告-深度转化] <|ad_begin|> + 22:28 [广告-点击] <|ad_begin|> +【2026-01-29】 + 06:34 [视频-点赞/长播] <|video_begin|> + 07:35 [广告-深度转化] <|ad_begin|> + 07:35 [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|> + 14:10 [广告-深度转化] <|ad_begin|> + 14:10 [广告-点击] <|ad_begin|> + 21:31 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + 02:10 [视频-收藏/长播] <|video_begin|> +【2026-02-01】 + 01:10 [广告-点击] <|ad_begin|> + 02:32 [广告-深度转化] <|ad_begin|> + 02:32 [广告-点击] <|ad_begin|> + 03:41 [广告-深度转化] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + 07:23 [广告-深度转化] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:35 [广告-深度转化] <|ad_begin|> + 08:35 [广告-点击] <|ad_begin|> + 09:13 [广告-深度转化] <|ad_begin|> + 09:13 [广告-点击] <|ad_begin|> + 09:33 [广告-深度转化] <|ad_begin|> + 09:33 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:14 [视频-点赞/长播] <|video_begin|> + 20:26 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 03:17 [广告-深度转化] <|ad_begin|> + 03:17 [广告-点击] <|ad_begin|> + 03:40 [广告-深度转化] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 07:17 [广告-深度转化] <|ad_begin|> + 07:17 [广告-点击] <|ad_begin|> + 07:42 [广告-深度转化] <|ad_begin|> + 07:42 [广告-点击] <|ad_begin|> + 08:40 [广告-深度转化] <|ad_begin|> + 08:40 [广告-点击] <|ad_begin|> + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 13:08 [广告-深度转化] <|ad_begin|> + 13:08 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:34 [广告-深度转化] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> + 21:49 [广告-点击] <|ad_begin|> + 21:50 [广告-点击] <|ad_begin|> + 22:09 [广告-深度转化] <|ad_begin|> + 22:09 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:51 [广告-深度转化] <|ad_begin|> + 00:51 [广告-点击] <|ad_begin|> + 01:41 [广告-深度转化] <|ad_begin|> + 01:41 [广告-点击] <|ad_begin|> + 06:04 [广告-深度转化] <|ad_begin|> + 06:04 [广告-点击] <|ad_begin|> + 09:22 [广告-深度转化] <|ad_begin|> + 09:22 [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|> +【2026-02-05】 + 03:13 [广告-深度转化] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:26 [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|> + 04:01 [视频-长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:03 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 10:33 [视频-长播] <|video_begin|> + 10:33 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [视频-点赞/长播] <|video_begin|> + 10:38 [视频-长播] <|video_begin|> + 10:38 [视频-长播] <|video_begin|> + 10:38 [视频-长播] <|video_begin|> + 10:45 [视频-长播] <|video_begin|> + 10:45 [视频-长播] <|video_begin|> + 10:46 [视频-长播] <|video_begin|> + 10:46 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-浏览] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:47 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 10:59 [视频-浏览] <|video_begin|> + 11:09 [视频-长播] <|video_begin|> + 11:09 [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）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心逻辑要求（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 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 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请针对以下主题提取行为逻辑链： +主题：从被动娱乐到主动探索特定兴趣品类 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动娱乐到主动探索特定兴趣品类", + "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": "从被动娱乐到主动探索特定兴趣品类", "events": [{"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣触发：用户被动接收并点击了以“赌石”为核心题材的短剧广告，这是对该主题的初步兴趣激发。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣泛化与场景联想：从虚拟的“赌石”交易联想到现实中的“市集交易”场景，用户开始主动观看景德镇夜市的视频，兴趣从戏剧化内容转向真实的生活化场景。"}, {"date": "2026-02-05", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "兴趣深化：在市集场景中，用户的注意力进一步聚焦到“卤菜熟食制作”这一具体的手工艺和美食门类，并通过点赞行为表达了更强的偏好。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知跃迁与消费联结：用户的兴趣从观看熟食制作，跃迁至对具体食材“东北红松子”的品质关注，视频内容直接关联到商品属性（饱满、皮薄）和物流服务，完成了从内容消费到潜在商品消费的认知连接。"}]}}<|im_end|> +<|im_start|>user +你擅长理解快手用户跨场景行为和语义ID表示，请根据输入信息归纳该用户的目标内容。 +已知如下用户跨场景行为： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>，首次打赏了 <|living_begin|>。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|> 有过关注/转发/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|> 有过关注/点赞/长播行为，看过视频 <|video_begin|>。 +广告行为: 对广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|> 完成过深度转化，点击过广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，收到您的指令。作为推荐系统分析助手，我将根据用户历史行为进行分析，并提供我的洞察与预测。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： +1. **成人情感与猎奇内容**：这是用户最突出且交互最深的内容偏好。用户在近两个月内有数十次针对此类广告的深度转化和点击行为如<|ad_begin|>、<|ad_begin|>，内容多以“免费小说/短剧”为载体，以性感美女为视觉元素，并暗示家庭、职场中的禁忌或冲突情节。同时，用户的多次搜索行为(如 2025-12-31, 2026-01-05)也高度指向这一领域，表现出强烈且持续的探索欲。 +2. **男性健康与养生**：这是用户在消费领域最核心的关注点。用户在一年内有超过20次购买和点击行为集中于缓解体力疲劳、补肾、增强免疫力等男性保健品(如<|prod_begin|> +该用户最近感兴趣的广告有: <|ad_begin|><|im_end|> +<|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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+我的分析重点放在了寻找一个从“看”到“做”的转变点上。在浏览2026年2月3日的行为时，我发现了一个非常清晰的信号。在当天上午08:20，用户观看并评论了一个展示手工编织围巾的视频 <|video_begin|>。这个视频强调了手工制品所蕴含的“情感价值”和“人情味”，这是一种情感层面的触动，而不仅仅是展示商品。我推测，这种情感化的内容很可能激发了用户自己动手制作的念头。 + +紧接着，我发现用户的行为立刻印证了我的推测。就在观看完视频之后，同一天内，她开始密集地点击与“手工编织”相关的商品。例如，她点击了标题中包含“手工长绒毛围巾diy自制编织心意礼物”的商品 <|prod_begin|>，以及多款以“蜡笔小新”为主题的DIY围巾材料包。这些点击行为中的关键词，如“diy”、“手工编织”、“心意礼物”，与她刚刚观看的视频内容高度契合。这表明她的兴趣已经从被动的观看，迅速转化为主动的、有明确目的的商品调研。她不再只是欣赏成品，而是在寻找能够让她亲身实践的工具和材料。 + +最终，在经过一番浏览和比较之后，用户在当天就完成了购买决策。她购买了一款名为“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包”的商品 <|prod_begin|>。这个购买行为是整个兴趣链条的关键闭环，标志着她完成了从被动的信息接收者到主动的实践者的身份转变。整个过程从看到视频激发兴趣，到搜索和比较DIY材料，再到最终下单购买，逻辑清晰，时间紧凑，构成了一条完整的兴趣唤醒与实践路径。 + +综合以上分析，我认为用户的行为清晰地展示了从被动接收信息到主动实践的手工兴趣唤醒过程。这个过程始于观看一个强调情感价值的手工编织视频 <|video_begin|>，这激发了她的制作兴趣；随后，她立即开始浏览具体的DIY围巾材料包商品，例如点击了“手工长绒毛围巾diy自制编织心意礼物” <|prod_begin|>，将模糊的兴趣转化为明确的行动意图；最终，通过购买“长毛手混线温暖灰羽手工编织diy棒针元宝织围巾材料包” <|prod_begin|>，完成了从认知到行为的转化。因此，未来向她推荐更多DIY手工制作类的内容或商品，有较大概率被吸引。 +{"logic_chain": {"name": "从被动接收信息到主动实践的手工兴趣唤醒", "events": [{"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣触发：被动观看到手工编织围巾的视频，内容强调“情感价值”和“人情味”，可能激发了用户的制作兴趣。"}, {"date": "2026-02-03", "action": "[商品-点击] <|prod_begin|>", "logic": "意图确认与调研：观看视频后，用户立即开始浏览具体的“DIY材料包”商品，关键词“手工”、“diy自制”、“心意礼物”与视频内容高度关联，表明兴趣已转化为具体行动意图。"}, {"date": "2026-02-03", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在浏览了多种DIY围巾材料包后，用户最终完成购买，实现了从“被动观看”到“主动实践”的认知与行为跃迁。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从泛化需求到精准定位的染发产品探索", "events": [{"date": "2025-10-10", "action": "[商品-购买] <|prod_begin|>", "logic": "初始行为：购买通用型染发产品，满足基础的染发需求。"}, {"date": "2026-01-04", "action": "[商品-购买] <|prod_begin|>", "logic": "需求聚焦与路径修正：意识到常规染发无法应对即时的白发问题，转向购买“遮盖白发神器”这种功能更具体、使用更便捷的“美发棒”产品，体现了对“补染”这一细分场景的认知增量。"}, {"date": "2026-01-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策优化与品质升级：在尝试了便捷性产品后，回归到染发膏品类，但选择了带有“二代升级”、“单剂”、“护染”等关键词的产品，表明用户在追求便捷的同时，开始关注产品的升级换代和养护功能，需求从“遮盖”深化为“便捷且护发”的更高阶需求。"}]}}<|im_end|> + + +{"logic_chain":{"name":"从娱乐消遣到寻求专业法律咨询的认知跃迁","events":[{"date":"2025-12-22","action":"[广告-深度转化] <|ad_begin|>","logic":"问题域接触：通过观看大量关于“出轨”、“情感纠葛”、“家庭矛盾”等主题的短剧广告，被动地接触并沉浸在相关问题情境中。"},{"date":"2026-01-06","action":"[直播-首次打赏] <|living_begin|>","logic":"认知跃迁与路径修正：用户的兴趣点从虚构的剧情消费，转向了现实中的专业解决方案。主动对提供“刑案咨询”和“实务讲解”的直播主进行打赏，表明其需求从娱乐消遣升级为获取专业法律知识，是典型的认知跃迁。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从被动娱乐到主动探索特定兴趣品类", "events": [{"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣触发：用户被动接收并点击了以“赌石”为核心题材的短剧广告，这是对该主题的初步兴趣激发。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣泛化与场景联想：从虚拟的“赌石”交易联想到现实中的“市集交易”场景，用户开始主动观看景德镇夜市的视频，兴趣从戏剧化内容转向真实的生活化场景。"}, {"date": "2026-02-05", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "兴趣深化：在市集场景中，用户的注意力进一步聚焦到“卤菜熟食制作”这一具体的手工艺和美食门类，并通过点赞行为表达了更强的偏好。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知跃迁与消费联结：用户的兴趣从观看熟食制作，跃迁至对具体食材“东北红松子”的品质关注，视频内容直接关联到商品属性（饱满、皮薄）和物流服务，完成了从内容消费到潜在商品消费的认知连接。"}]}}<|im_end|> +好的，收到您的指令。作为推荐系统分析助手，我将根据用户历史行为进行分析，并提供我的洞察与预测。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： +1. **成人情感与猎奇内容**：这是用户最突出且交互最深的内容偏好。用户在近两个月内有数十次针对此类广告的深度转化和点击行为如<|ad_begin|>、<|ad_begin|>，内容多以“免费小说/短剧”为载体，以性感美女为视觉元素，并暗示家庭、职场中的禁忌或冲突情节。同时，用户的多次搜索行为(如 2025-12-31, 2026-01-05)也高度指向这一领域，表现出强烈且持续的探索欲。 +2. **男性健康与养生**：这是用户在消费领域最核心的关注点。用户在一年内有超过20次购买和点击行为集中于缓解体力疲劳、补肾、增强免疫力等男性保健品(如<|prod_begin|> +该用户最近感兴趣的广告有: <|ad_begin|><|im_end|> +[INFO|configuration_utils.py:769] 2026-07-08 03:16:26,494 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/config.json +[INFO|configuration_utils.py:847] 2026-07-08 03:16:26,495 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +[INFO|2026-07-08 03:16:26] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-08 03:16:26] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-08 03:16:26,815 >> loading weights file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-08 03:16:26,815 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-08 03:16:26,958 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-08 03:16:28,137 >> Generate config GenerationConfig { + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-08 03:16:28,137 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v12_user_cot_focus_lr15e6. +[INFO|2026-07-08 03:16:28] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-08 03:16:28] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-08 03:16:28] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-08 03:16:28] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-08 03:16:28] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-08 03:16:28,264 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-08 03:16:28,264 >> Num examples = 2,267 +[INFO|trainer.py:1471] 2026-07-08 03:16:28,264 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-08 03:16:28,264 >> Num update steps per epoch = 567 +[INFO|trainer.py:1473] 2026-07-08 03:16:28,264 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-08 03:16:28,264 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-08 03:16:28,264 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-08 03:16:28,264 >> Total optimization steps = 284 +[INFO|trainer.py:1479] 2026-07-08 03:16:28,264 >> Number of trainable parameters = 801,433,600 + 0%| | 0/284 [00:00> `use_return_dict` is deprecated! 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03:55:28,051 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 284/284 [38:59<00:00, 8.39s/it] 100%|██████████| 284/284 [38:59<00:00, 8.24s/it] +[INFO|trainer.py:3815] 2026-07-08 03:55:28,063 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05 +[INFO|configuration_utils.py:533] 2026-07-08 03:55:28,065 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/config.json +[INFO|configuration_utils.py:816] 2026-07-08 03:55:28,065 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/generation_config.json +{'loss': '1.533', 'grad_norm': '3.422', 'learning_rate': '3.333e-06', 'epoch': '0.008822'} +{'loss': '1.587', 'grad_norm': '3.375', 'learning_rate': '4.999e-06', 'epoch': '0.01764'} +{'loss': '1.515', 'grad_norm': '5.469', 'learning_rate': '4.99e-06', 'epoch': '0.02647'} +{'loss': '1.529', 'grad_norm': '4.188', 'learning_rate': '4.973e-06', 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'grad_norm': '3.219', 'learning_rate': '2.511e-07', 'epoch': '0.4323'} +{'loss': '1.597', 'grad_norm': '1.977', 'learning_rate': '1.93e-07', 'epoch': '0.4411'} +{'loss': '1.5', 'grad_norm': '2.609', 'learning_rate': '1.423e-07', 'epoch': '0.4499'} +{'loss': '1.487', 'grad_norm': '2.156', 'learning_rate': '9.911e-08', 'epoch': '0.4588'} +{'loss': '1.455', 'grad_norm': '2.234', 'learning_rate': '6.358e-08', 'epoch': '0.4676'} +{'loss': '1.528', 'grad_norm': '5.406', 'learning_rate': '3.583e-08', 'epoch': '0.4764'} +{'loss': '1.485', 'grad_norm': '4.625', 'learning_rate': '1.595e-08', 'epoch': '0.4852'} +{'loss': '1.511', 'grad_norm': '2.75', 'learning_rate': '3.99e-09', 'epoch': '0.494'} +{'train_runtime': '2340', 'train_samples_per_second': '0.484', 'train_steps_per_second': '0.121', 'train_loss': '1.508', 'epoch': '0.5011'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-08 03:55:29,070 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-08 03:55:29,093 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/tokenizer_config.json +***** train metrics ***** + epoch = 0.5011 + total_flos = 127639450GF + train_loss = 1.5081 + train_runtime = 0:38:59.79 + train_samples_per_second = 0.484 + train_steps_per_second = 0.121 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v18_v12_unify_replay_lr5e6_ep05/training_loss.png +[WARNING|2026-07-08 03:55:29] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-08 03:55:29] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-08 03:55:29,192 >> 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..432f859 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.5011027790030878, + "total_flos": 1.3705181662943232e+17, + "train_loss": 1.5080752087311007, + "train_runtime": 2339.7979, + "train_samples_per_second": 0.484, + "train_steps_per_second": 0.121 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..a8907c1 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,57 @@ +{"current_steps": 5, "total_steps": 284, "loss": 1.5331814765930176, "lr": 3.3333333333333333e-06, "epoch": 0.00882223202470225, "percentage": 1.76, "elapsed_time": "0:00:41", "remaining_time": "0:38:37"} +{"current_steps": 10, "total_steps": 284, "loss": 1.5866724014282227, "lr": 4.998563448899413e-06, "epoch": 0.0176444640494045, "percentage": 3.52, "elapsed_time": "0:01:22", 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