From 983e800befe487d03e51dab7049e1605be9c5ff9 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 29 Aug 2026 13:10:17 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/v44_v29_clean_live_temporal_lr6e7_ep045 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 102 + generation_config.json | 12 + model.safetensors | 3 + ...36608.ubuntu-System-Product-Name.1408305.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2245 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 5 + trainer_state.json | 71 + training_args.bin | 3 + training_loss.png | Bin 0 -> 34775 bytes 17 files changed, 2725 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 experiment_recipe.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul16_01-30-08_ubuntu-System-Product-Name/events.out.tfevents.1784136608.ubuntu-System-Product-Name.1408305.0 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 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merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..bf2ff2b --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v29_v19_user_world_guard_lr8e7_ep018 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v44_v29_clean_live_temporal_lr6e7_ep045 + results: [] +--- + + + +# v44_v29_clean_live_temporal_lr6e7_ep045 + +This model is a fine-tuned version of [lldois/v29_v19_user_world_guard_lr8e7_ep018](https://huggingface.co/lldois/v29_v19_user_world_guard_lr8e7_ep018) on the exp_v44_clean_live_temporal dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 6e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607194 +- 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: 0.45 + +### 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..6a325a1 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.4584527220630373, + "total_flos": 1.8995601921736704e+16, + "train_loss": 2.0322839260101317, + "train_runtime": 332.2523, + "train_samples_per_second": 0.473, + "train_steps_per_second": 0.12 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..ae722cc --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,102 @@ +{ + "created_at": "2026-07-16 01:45:38", + "name": "v44_v29_clean_live_temporal_lr6e7_ep045", + "base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v44_clean_live_temporal", + "dataset_manifest": { + "name": "v44_clean_live_temporal", + "path": "/home/ll/llm4rec/experiments/data/v44_clean_live_temporal.jsonl", + "records": 9970, + "bytes": 51683737, + "sha256": "b5dca87857669c62efbfc40d14a3e6d71b0ecd3b6b3d848e54f88b469253a1c3", + "requested_components": { + "r3_live_specialist_direct": 1300, + "r3_live_specialist_think": 1800, + "old_rec_live_direct": 350, + "old_rec_live_think": 250, + "old_rec_ad_direct": 250, + "old_rec_ad_think": 100, + "old_rec_goods_direct": 250, + "old_rec_goods_think": 100, + "old_rec_video_direct": 500, + "old_rec_video_think": 200, + "old_user_array_direct": 600, + "old_user_logic_direct": 550, + "old_user_logic_think": 180, + "old_item_goods_direct": 500, + "old_item_goods_think": 120, + "old_item_live_direct": 350, + "old_item_live_think": 100, + "old_item_ad_direct": 500, + "old_item_ad_think": 120, + "old_item_video_direct": 500, + "old_item_video_think": 100, + "general_cn_direct": 500, + "general_cn_think": 250, + "general_other_direct": 350, + "general_other_think": 150 + }, + "groups": { + "old_rec_clean": 2000, + "old_user_clean": 1330, + "old_item_clean": 2290, + "r3_clean": 3100, + "general_clean": 1250 + }, + "variants": { + "old_rec_video_direct": 500, + "old_user_array_direct": 600, + "old_item_video_direct": 500, + "strict_live_direct": 1300, + "strict_live_cot": 1800, + "old_item_ad_direct": 500, + "strict_single_turn_direct": 850, + "old_item_goods_cot": 120, + "old_rec_video_cot": 200, + "strict_single_turn_cot": 400, + "old_rec_live_cot": 250, + "old_rec_ad_direct": 250, + "old_rec_live_direct": 350, + "old_user_logic_direct": 550, + "old_item_live_direct": 350, + "old_rec_goods_cot": 100, + "old_rec_ad_cot": 100, + "old_item_goods_direct": 500, + "old_item_ad_cot": 120, + "old_user_logic_cot": 180, + "old_item_live_cot": 100, + "old_rec_goods_direct": 250, + "old_item_video_cot": 100 + }, + "routes": { + "no_think": 6500, + "think": 3470 + }, + "domains": { + "video": 1300, + "live": 4150, + "ad": 970, + "goods": 970 + }, + "canonical_duplicates": 0, + "output_chars_p50": 108, + "output_chars_p90": 950, + "seed": 202607251 + }, + "learning_rate": "6.0e-7", + "epochs": 0.45, + "stages": null, + "note": "Strict live specialist: future high-value live target, chronological pre-target history, semantic support, and 58/42 CoT/direct live supervision with clean guards.", + "cot_policy": "Preserve meaningful CoT for /think and pure final outputs for /no_think.", + "checkpoint_policy": "save_strategy=no; only the final model is retained.", + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "78d5f1619c271f19b4204ff4fa210ff4bbbadd81cdc0221e5f40cee201fc9877", + "data_builder": "/home/ll/llm4rec/experiments/source_data.py", + "data_builder_sha256": "a689f0bf36e31f612c3800f971501655b403e38420f4aad9116a3110206a1d5c", + "data_cleaner": "/home/ll/llm4rec/experiments/clean_data.py", + "data_cleaner_sha256": "c1831a3d85c86c740ba80e095252574495b06d79d6341ed190ac2e3fe67133bf", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v44_v29_clean_live_temporal_lr6e7_ep045.yaml" + ], + "reproduce": 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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..e745228 --- /dev/null +++ b/train.log @@ -0,0 +1,2245 @@ +[2026-07-16 01:29:43] stage 1/1: /home/ll/llm4rec/experiments/configs/v44_v29_clean_live_temporal_lr6e7_ep045.yaml +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-16 01:29:46] 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] 2026-07-16 01:29:46,378 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:29:46,380 >> 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] 2026-07-16 01:29:47,066 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:29:47,067 >> 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] 2026-07-16 01:29:47,067 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:29:47,067 >> 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|2026-07-16 01:29:47] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v44_clean_live_temporal.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: 9970 examples [00:00, 161626.15 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/9970 [00:00user +【用户交互历史】： +【2025-01-11】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-12】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-01-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-12】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-04】 + --:-- [直播-关注] <|living_begin|> +【2025-10-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-07】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [直播-关注] <|living_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [直播-关注] <|living_begin|> +【2025-10-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> +【2025-10-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-16】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-31】 + 07:10 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + 11:50 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 01:18 [广告-深度转化] <|ad_begin|> + 12:30 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + 12:44 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 15:00 [视频-评论/点赞/长播] <|video_begin|> + 15:08 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-23】 + 15:59 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 15:56 [视频-关注/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 18:58 [视频-评论/点赞/长播] <|video_begin|> + 18:58 [视频-评论/点赞/长播] <|video_begin|> + 18:58 [视频-评论/点赞/长播] <|video_begin|> + 19:08 [视频-评论/点赞/长播] <|video_begin|> + 19:10 [视频-评论/点赞/长播] <|video_begin|> + 19:10 [视频-评论/点赞/长播] <|video_begin|> + 19:34 [视频-点赞/长播] <|video_begin|> + 19:34 [视频-点赞/长播] <|video_begin|> + 19:42 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + 13:11 [视频-点赞/长播] <|video_begin|> + 13:14 [视频-点赞/长播] <|video_begin|> + 14:01 [视频-点赞/长播] <|video_begin|> + 15:53 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + 16:07 [视频-点赞/长播] <|video_begin|> + 16:46 [视频-评论/点赞/长播] <|video_begin|> + 16:46 [视频-点赞/长播] <|video_begin|> + 16:46 [视频-点赞/长播] <|video_begin|> + 17:01 [视频-转发/长播] <|video_begin|> + 18:19 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 05:40 [广告-深度转化] <|ad_begin|> + 12:45 [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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 16:02 [视频-评论/点赞/长播] <|video_begin|> + 16:02 [视频-点赞/长播] <|video_begin|> + 16:10 [视频-评论/点赞/长播] <|video_begin|> + 16:10 [视频-点赞/长播] <|video_begin|> + 16:18 [视频-点赞/长播] <|video_begin|> + 16:24 [视频-点赞/长播] <|video_begin|> + 16:38 [视频-评论/长播] <|video_begin|> + 16:55 [视频-点赞/长播] <|video_begin|> + 17:01 [视频-点赞/长播] <|video_begin|> + 17:14 [视频-点赞/长播] <|video_begin|> + 17:14 [视频-点赞/长播] <|video_begin|> + 18:45 [视频-评论/长播] <|video_begin|> + 18:54 [视频-点赞/长播] <|video_begin|> + 18:54 [视频-点赞/长播] <|video_begin|> +【2026-01-29】 + 10:34 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-30】 + 04:04 [视频-点赞/长播] <|video_begin|> + 04:10 [视频-长播] <|video_begin|> + 06:28 [广告-点击] <|ad_begin|> + 06:54 [广告-点击] <|ad_begin|> + 06:55 [广告-点击] <|ad_begin|> + 06:56 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 07:00 [广告-点击] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:06 [广告-点击] <|ad_begin|> + 07:06 [广告-点击] <|ad_begin|> + 07:48 [广告-点击] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> + 08:03 [广告-点击] <|ad_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:58 [广告-点击] <|ad_begin|> + 08:59 [广告-点击] <|ad_begin|> + 09:02 [广告-点击] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> + 13:08 [广告-点击] <|ad_begin|> + 13:10 [广告-点击] <|ad_begin|> + 13:16 [广告-点击] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 14:33 [广告-点击] <|ad_begin|> + 15:06 [广告-点击] <|ad_begin|> + 15:44 [广告-点击] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + 02:51 [广告-点击] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 02:58 [广告-点击] <|ad_begin|> + 03:00 [广告-点击] <|ad_begin|> + 03:05 [视频-长播] <|video_begin|> + 03:05 [视频-长播] <|video_begin|> + 05:13 [广告-点击] <|ad_begin|> + 05:20 [广告-点击] <|ad_begin|> + 05:29 [视频-点赞/长播] <|video_begin|> + 05:32 [广告-点击] <|ad_begin|> + 07:09 [广告-点击] <|ad_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 08:03 [视频-长播] <|video_begin|> + 08:03 [视频-长播] <|video_begin|> + 08:57 [广告-点击] <|ad_begin|> + 09:26 [视频-长播] <|video_begin|> + 09:26 [视频-长播] <|video_begin|> + 10:04 [视频-长播] <|video_begin|> + 10:04 [视频-长播] <|video_begin|> + 10:04 [视频-长播] <|video_begin|> + 10:04 [视频-长播] <|video_begin|> + 12:20 [广告-点击] <|ad_begin|> + 12:23 [广告-点击] <|ad_begin|> + 12:30 [广告-点击] <|ad_begin|> + 12:31 [视频-长播] <|video_begin|> + 13:22 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 14:25 [广告-点击] <|ad_begin|> + 14:25 [广告-点击] <|ad_begin|> + 15:29 [广告-点击] <|ad_begin|> + 15:31 [广告-点击] <|ad_begin|> + 15:43 [广告-点击] <|ad_begin|> + 15:45 [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-购买] <|prod_begin|> + 18:14 [视频-长播] <|video_begin|> +【2026-02-01】 + 01:57 [广告-点击] <|ad_begin|> + 01:58 [广告-点击] <|ad_begin|> + 01:59 [广告-点击] <|ad_begin|> + 02:00 [广告-点击] <|ad_begin|> + 02:02 [广告-点击] <|ad_begin|> + 02:03 [广告-点击] <|ad_begin|> + 02:35 [视频-点赞/长播] <|video_begin|> + 05:59 [广告-点击] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 06:36 [视频-长播] <|video_begin|> + 08:37 [广告-点击] <|ad_begin|> + 14:26 [视频-长播] <|video_begin|> + 15:02 [视频-评论/点赞/长播] <|video_begin|> + 15:02 [视频-点赞/长播] <|video_begin|> + 15:12 [视频-点赞/长播] <|video_begin|> + 15:12 [视频-点赞/长播] <|video_begin|> + 15:12 [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-购买] <|prod_begin|> + 16:53 [广告-点击] <|ad_begin|> + 17:13 [视频-点赞/长播] <|video_begin|> + 17:13 [视频-点赞/长播] <|video_begin|> + 17:28 [视频-点赞/长播] <|video_begin|> + 17:40 [视频-长播] <|video_begin|> + 17:46 [视频-长播] <|video_begin|> + 17:52 [广告-点击] <|ad_begin|> + 17:54 [广告-点击] <|ad_begin|> + 18:08 [视频-点赞/长播] <|video_begin|> + 18:22 [视频-点赞/长播] <|video_begin|> + 18:31 [视频-长播] <|video_begin|> + 18:33 [视频-长播] <|video_begin|> + 18:39 [视频-点赞/长播] <|video_begin|> + 18:39 [视频-点赞/长播] <|video_begin|> + 18:45 [视频-长播] <|video_begin|> + 18:45 [视频-点赞/长播] <|video_begin|> + 18:55 [视频-长播] <|video_begin|> +【2026-02-02】 + 15:09 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 17:41 [视频-评论/点赞/长播] <|video_begin|> + 17:41 [视频-点赞/长播] <|video_begin|> + 17:49 [视频-评论/点赞/长播] <|video_begin|> + 17:49 [视频-评论/长播] <|video_begin|> + 18:00 [视频-评论/点赞/长播] <|video_begin|> + 18:04 [视频-评论/点赞/长播] <|video_begin|> + 18:14 [视频-评论/点赞/长播] <|video_begin|> + 18:14 [视频-评论/点赞/长播] <|video_begin|> + 18:26 [视频-长播] <|video_begin|> + 18:36 [视频-长播] <|video_begin|> + 18:45 [视频-长播] <|video_begin|> + 18:45 [视频-点赞/长播] <|video_begin|> + 18:59 [视频-评论/点赞/长播] <|video_begin|> + 19:02 [视频-评论/点赞/长播] <|video_begin|> + 19:10 [视频-点赞/长播] <|video_begin|> + 19:16 [视频-长播] <|video_begin|> + 19:37 [视频-长播] <|video_begin|> + 19:52 [视频-长播] <|video_begin|> + 19:55 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 05:49 [广告-点击] <|ad_begin|> + 05:53 [广告-点击] <|ad_begin|> + 05:54 [广告-点击] <|ad_begin|> + 10:50 [广告-点击] <|ad_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-点赞/长播] <|video_begin|> + 14:18 [视频-点赞/长播] <|video_begin|> + 14:36 [视频-点赞/长播] <|video_begin|> + 14:38 [广告-点击] <|ad_begin|> + 14:39 [广告-点击] <|ad_begin|> + 14:43 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 18:40 [广告-点击] <|ad_begin|> + 21:30 [视频-长播] <|video_begin|> + 21:30 [视频-长播] <|video_begin|> + 21:30 [视频-长播] <|video_begin|> + 21:30 [视频-长播] <|video_begin|> +【2026-02-04】 + 01:43 [广告-点击] <|ad_begin|> + 10:29 [视频-长播] <|video_begin|> + 10:29 [视频-长播] <|video_begin|> + 10:29 [视频-长播] <|video_begin|> + 10:40 [视频-长播] <|video_begin|> + 15:45 [广告-点击] <|ad_begin|> + 15:47 [视频-长播] <|video_begin|> + 15:47 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:34 [视频-长播] <|video_begin|> + 16:50 [视频-长播] <|video_begin|> + 17:08 [视频-长播] <|video_begin|> + 17:08 [视频-评论/点赞/长播] <|video_begin|> + 17:08 [视频-点赞/长播] <|video_begin|> + 17:19 [视频-长播] <|video_begin|> + 17:19 [视频-长播] <|video_begin|> + 17:19 [视频-点赞/长播] <|video_begin|> + 18:25 [视频-长播] <|video_begin|> + 18:25 [视频-点赞/长播] <|video_begin|> +【2026-02-05】 + 14:27 [视频-点赞/长播] <|video_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家和数据格式化专家，请基于以上用户交互历史，围绕指定主题提取具备行为递进关系与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心抽取逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 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。 +请围绕以下主题提取行为逻辑链： +主题：从被动接收到主动探索的健康焦虑演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收到主动探索的健康焦虑演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/think<|im_end|> +<|im_start|>assistant + +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，挖掘出一条关于“从被动接收到主动探索的健康焦虑演化”的行为链。我需要按照时间顺序，找出那些在场景、兴趣或需求上有递进关系的行为，并最终总结出符合任务要求的逻辑链。 我首先注意到，用户的行为时间线跨度很大，从2025年初一直延续到2026年初。在分析初期，我发现用户的兴趣点非常广泛，从美女/帅哥直播，到文化收藏，再到各种日用品和工具的购买，显得有些分散。然而，当我把目光聚焦在“健康”这个主题上时，一条有趣的线索开始浮现，这条线索并非关于用户自身的健康，而是指向了动物或养殖领域。 我的分析起点是2026年1月30日的一次广告点击行为。在这一天，用户点击了一则关于如何处理小猫软便和饮水问题的广告 <|ad_begin|>。虽然这只是一个简单的点击，但它像一颗石子投入水中，激起了涟漪。我推测，这个广告可能首次将“动物健康”这一具体问题推送到用户面前，触发了他对这类问题的初步认知。这是一种被动的信息接收，可能源于偶然的推送，也可能用户家中确实有宠物或对此类话题有潜在的关注。 顺着这条线索，我回溯了用户的购买历史，试图寻找与此相关的行为。我发现在2025年11月，也就是在点击那则宠物健康广告之前，用户已经有过一些非常专业的购买行为。具体来说，在11月17日，他购买了黄芪多糖 <|prod_begin|>，这是一种常用于提升猪、牛、羊等禽畜免疫力的产品。这个行为让我感到惊讶，因为它远比处理小猫软便要专业得多。这表明用户对动物健康的关注并非一时兴起，而是已经进入了主动寻求解决方案的阶段，并且关注点是更大型的养殖动物，而非普通宠物。 紧接着，在两天后的11月19日，用户的行为进一步印证了我的推测。他购买了一款名为“犊牛快长”的浓缩加强型产品 <|prod_begin|>。这次购买显示出他的需求变得更加具体和聚焦。 + +{"logic_chain":{"name":"从被动接收到主动探索的健康焦虑演化","events":[{"date":"2026-01-30","action":"[广告-点击] <|ad_begin|>","logic":"问题触发：被动接收到关于“宠物软便”和“饮水问题”的广告信息，这构成了用户对动物健康问题的初始认知输入或问题意识的唤醒。"},{"date":"2025-11-17","action":"[商品-购买] <|prod_begin|>","logic":"解决方案探索（免疫调节）：在接触到宠物健康信息后，用户购买了“黄芪多糖”，这是一种常用于提升畜禽免疫力的中兽药成分。行为从泛化的健康知识转向了具体的药物成分购买，显示出用户开始主动寻求解决方案。"},{"date":"2025-11-19","action":"[商品-购买] <|prod_begin|>","logic":"需求聚焦（特定功能）：在购买了普适性的免疫调节产品后，用户的需求进一步聚焦到“犊牛快长”这类更具针对性的生长发育类产品上。这表明用户的关注点从“防病”向“促生长”深化，需求更加具体化。"},{"date":"2025-11-30","action":"[直播-关注] <|living_begin|>","logic":"认知深化与信息渠道升级：在连续购买了两种专业兽药后，用户开始关注专门讲解“林可霉素”、“氟尼辛”等更专业抗生素的“兽药推荐达人”直播。这标志着用户的认知从“买产品”跃迁至“学知识”，主动寻求专业人士的指导，形成了从被动接收信息到主动购买产品，再到寻求专业知识的完整闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-07-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> +【2025-07-21】 + --:-- [直播-关注] <|living_begin|> +【2025-07-22】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-26】 + --:-- [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|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-04】 + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-04】 + --:-- [直播-关注] <|living_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-01】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-28】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + 02:54 [广告-深度转化] <|ad_begin|> + 03:46 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 08:33 [广告-深度转化] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 14:53 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 10:01 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 00:29 [广告-深度转化] <|ad_begin|> + 00:40 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 09:50 [广告-深度转化] <|ad_begin|> + 11:58 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 11:44 [广告-深度转化] <|ad_begin|> + 12:09 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> + 17:05 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 11:29 [广告-深度转化] <|ad_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 16:14 [广告-深度转化] <|ad_begin|> + 17:00 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 10:44 [广告-深度转化] <|ad_begin|> + 13:12 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 06:49 [广告-深度转化] <|ad_begin|> + 18:38 [广告-深度转化] <|ad_begin|> + 19:15 [广告-深度转化] <|ad_begin|> + 19:41 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 01:52 [广告-深度转化] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 05:04 [广告-深度转化] <|ad_begin|> + 16:17 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:30 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 02:48 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 06:12 [广告-深度转化] <|ad_begin|> + 08:20 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 05:16 [广告-深度转化] <|ad_begin|> + 08:17 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> +【2026-01-14】 + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:32 [广告-深度转化] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> + 14:28 [广告-点击] <|ad_begin|> +【2026-01-15】 + 03:23 [广告-点击] <|ad_begin|> + 13:04 [广告-深度转化] <|ad_begin|> + 13:04 [广告-点击] <|ad_begin|> +【2026-01-16】 + 04:24 [广告-深度转化] <|ad_begin|> + 04:24 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 16:20 [广告-深度转化] <|ad_begin|> + 16:20 [广告-点击] <|ad_begin|> + 20:35 [广告-点击] <|ad_begin|> +【2026-01-17】 + 01:27 [广告-深度转化] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 03:32 [广告-深度转化] <|ad_begin|> + 03:32 [广告-点击] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 05:36 [广告-点击] <|ad_begin|> + 07:13 [广告-深度转化] <|ad_begin|> + 07:13 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> + 13:33 [广告-点击] <|ad_begin|> + 14:47 [广告-深度转化] <|ad_begin|> + 14:47 [广告-点击] <|ad_begin|> +【2026-01-18】 + 08:11 [广告-深度转化] <|ad_begin|> + 08:11 [广告-点击] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> +【2026-01-19】 + 11:47 [广告-点击] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> +【2026-01-20】 + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> +【2026-01-21】 + 15:51 [广告-深度转化] <|ad_begin|> + 15:51 [广告-点击] <|ad_begin|> +【2026-01-22】 + 00:28 [广告-深度转化] <|ad_begin|> + 00:28 [广告-点击] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + 04:37 [广告-深度转化] <|ad_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:41 [广告-点击] <|ad_begin|> + 14:46 [广告-点击] <|ad_begin|> + 17:44 [广告-点击] <|ad_begin|> + 20:38 [广告-点击] <|ad_begin|> +【2026-01-24】 + --:-- [商品-购买] <|prod_begin|> + 07:31 [广告-点击] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:12 [广告-点击] <|ad_begin|> + 12:37 [广告-点击] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:07 [广告-点击] <|ad_begin|> + 15:24 [广告-点击] <|ad_begin|> +【2026-01-25】 + 04:00 [广告-点击] <|ad_begin|> + 23:17 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:12 [广告-深度转化] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> + 18:12 [广告-深度转化] <|ad_begin|> + 18:12 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [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|> + 09:33 [广告-深度转化] <|ad_begin|> + 09:33 [广告-点击] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:03 [广告-点击] <|ad_begin|> + 14:27 [视频-评论/长播] <|video_begin|> + 15:32 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 15:12 [视频-评论/长播] <|video_begin|> + 15:16 [视频-评论/长播] <|video_begin|> + 16:05 [视频-评论/长播] <|video_begin|> + 16:13 [视频-评论/长播] <|video_begin|> + 16:24 [广告-点击] <|ad_begin|> + 17:46 [视频-转发/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|> + 04:18 [广告-点击] <|ad_begin|> +【2026-01-31】 + 05:00 [广告-深度转化] <|ad_begin|> + 05:00 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 14:54 [视频-转发/长播] <|video_begin|> + 17:10 [广告-点击] <|ad_begin|> + 17:11 [视频-评论/长播] <|video_begin|> + 17:40 [广告-点击] <|ad_begin|> +【2026-02-01】 + 00:48 [视频-评论/长播] <|video_begin|> + 02:09 [视频-评论/长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:30 [广告-点击] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 14:57 [视频-转发/点赞/长播] <|video_begin|> + 19:22 [视频-评论/长播] <|video_begin|> +【2026-02-02】 + 01:19 [视频-评论/长播] <|video_begin|> + 01:47 [视频-评论/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|> + 05:25 [视频-评论/长播] <|video_begin|> + 05:40 [广告-点击] <|ad_begin|> + 05:57 [广告-点击] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:47 [视频-评论/长播] <|video_begin|> + 08:10 [视频-评论/长播] <|video_begin|> + 12:04 [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|> + 06:37 [视频-评论/长播] <|video_begin|> + 06:47 [视频-浏览] <|video_begin|> + 06:54 [视频-评论/长播] <|video_begin|> + 07:01 [视频-评论/长播] <|video_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 13:23 [视频-评论/长播] <|video_begin|> + 14:47 [视频-评论/长播] <|video_begin|> + 15:20 [视频-评论/长播] <|video_begin|> + 16:58 [视频-评论/长播] <|video_begin|> + 17:21 [视频-评论/长播] <|video_begin|> + 17:39 [视频-浏览] <|video_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + 20:43 [视频-评论/长播] <|video_begin|> + 20:53 [视频-评论/长播] <|video_begin|> + 21:10 [视频-评论/长播] <|video_begin|> + 21:18 [视频-评论/长播] <|video_begin|> + 21:25 [视频-评论/长播] <|video_begin|> + 21:35 [视频-评论/长播] <|video_begin|> + 21:39 [视频-评论/长播] <|video_begin|> + 21:44 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:59 [视频-评论/长播] <|video_begin|> + 22:12 [视频-评论/长播] <|video_begin|> + 23:19 [广告-点击] <|ad_begin|> + 23:52 [视频-浏览] <|video_begin|> +【2026-02-05】 + 00:37 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 01:08 [视频-收藏/长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:36 [视频-评论/长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:54 [视频-评论/长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 02:06 [视频-评论/长播] <|video_begin|> + 02:06 [视频-长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + 02:25 [视频-评论/长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:30 [视频-长播] <|video_begin|> + 02:40 [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|> + 02:51 [视频-评论/长播] <|video_begin|> + 03:11 [视频-评论/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|> + 03:32 [视频-长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:37 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:09 [视频-评论/长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 05:45 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:32 [视频-评论/长播] <|video_begin|> + 06:32 [视频-长播] <|video_begin|> + 06:39 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:07 [视频-评论/长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:34 [视频-评论/长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 13:01 [广告-深度转化] <|ad_begin|> + 13:01 [广告-点击] <|ad_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心逻辑要求（递进性准则）： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 所延伸出的需求，严禁提取任何品类平级、内容重复或仅由天气/促销触发的并列凑单行为。 +1.场景驱动协同：交互 B 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +2.兴趣因果递进：交互 B 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在同一主题类目下，交互 B 的需求必须比交互 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。 +请根据以下主题提取行为逻辑链： +主题：从被动接收信息到主动探索知识的兴趣演化 + +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探索知识的兴趣演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从被动接收信息到主动探索知识的兴趣演化","events":[{"date":"2026-01-26","action":"[广告-深度转化] <|ad_begin|>","logic":"初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"},{"date":"2026-01-29","action":"[视频-转发/长播] <|video_begin|>","logic":"行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|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" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化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】： +【2024-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-05】 + --:-- [直播-打赏] <|living_begin|> +【2025-06-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-20】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-26】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-16】 + --:-- [直播-打赏] <|living_begin|> +【2025-08-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-11】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-13】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-14】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-26】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-29】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-02】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-02】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-04】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-14】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-16】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-17】 + --:-- [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|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [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] <|living_begin|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:10 [广告-点击] <|ad_begin|> + 09:37 [广告-深度转化] <|ad_begin|> + 09:37 [广告-点击] <|ad_begin|> + 12:39 [广告-深度转化] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> +【2025-12-11】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-12】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-19】 + --:-- [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|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + 01:52 [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/首次打赏] <|living_begin|> + 15:11 [广告-深度转化] <|ad_begin|> + 15:11 [广告-点击] <|ad_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-06】 + 03:53 [广告-深度转化] <|ad_begin|> + 03:53 [广告-点击] <|ad_begin|> +【2026-01-08】 + 07:21 [广告-深度转化] <|ad_begin|> + 07:21 [广告-点击] <|ad_begin|> + 07:28 [广告-深度转化] <|ad_begin|> + 07:28 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + 06:59 [广告-深度转化] <|ad_begin|> + 06:59 [广告-点击] <|ad_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 05:17 [广告-深度转化] <|ad_begin|> + 05:17 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 23:18 [广告-深度转化] <|ad_begin|> + 23:18 [广告-点击] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + 22:58 [视频-点赞/长播] <|video_begin|> + 22:58 [视频-点赞/长播] <|video_begin|> +【2026-01-22】 + 00:47 [视频-点赞/长播] <|video_begin|> + 00:56 [视频-点赞/长播] <|video_begin|> + 00:56 [视频-收藏/点赞/长播] <|video_begin|> + 01:08 [广告-深度转化] <|ad_begin|> + 01:08 [广告-点击] <|ad_begin|> + 23:10 [视频-点赞/长播] <|video_begin|> + 23:21 [广告-点击] <|ad_begin|> + 23:54 [视频-点赞/长播] <|video_begin|> + 23:54 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + 21:44 [视频-点赞/长播] <|video_begin|> + 21:53 [视频-点赞/长播] <|video_begin|> + 21:53 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + 01:31 [视频-收藏/点赞/长播] <|video_begin|> + 01:48 [视频-点赞/长播] <|video_begin|> + 14:31 [视频-点赞/长播] <|video_begin|> + 14:45 [视频-关注/点赞/长播] <|video_begin|> + 14:57 [视频-点赞/长播] <|video_begin|> + 14:57 [视频-点赞/长播] <|video_begin|> + 15:04 [视频-关注/收藏/点赞/长播] <|video_begin|> + 15:06 [视频-点赞/长播] <|video_begin|> + 15:09 [视频-关注/长播] <|video_begin|> + 15:14 [视频-关注/长播] <|video_begin|> + 15:14 [视频-关注/长播] <|video_begin|> +【2026-01-25】 + 01:12 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> + 04:46 [视频-关注/长播] <|video_begin|> + 05:22 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 07:32 [视频-点赞/长播] <|video_begin|> + 07:48 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 07:53 [视频-长播] <|video_begin|> + 09:36 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 00:45 [视频-长播] <|video_begin|> + 00:46 [视频-长播] <|video_begin|> + 01:05 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:33 [视频-长播] <|video_begin|> + 06:33 [视频-长播] <|video_begin|> + 06:33 [视频-点赞/长播] <|video_begin|> + 13:47 [视频-长播] <|video_begin|> + 13:47 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 15:38 [视频-长播] <|video_begin|> + 23:48 [视频-长播] <|video_begin|> + 23:58 [视频-长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 10:04 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:22 [视频-点赞/长播] <|video_begin|> + 10:31 [视频-点赞/长播] <|video_begin|> + 10:54 [视频-长播] <|video_begin|> + 10:54 [视频-长播] <|video_begin|> + 10:54 [视频-点赞/长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:00 [视频-长播] <|video_begin|> + 11:00 [视频-点赞/长播] <|video_begin|> + 11:00 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:43 [视频-点赞/长播] <|video_begin|> + 05:04 [视频-长播] <|video_begin|> + 05:04 [视频-点赞/长播] <|video_begin|> + 05:04 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 09:26 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 10:31 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 11:40 [视频-长播] <|video_begin|> + 11:40 [视频-点赞/长播] <|video_begin|> + 11:40 [视频-点赞/长播] <|video_begin|> + 11:46 [视频-点赞/长播] <|video_begin|> + 13:14 [视频-长播] <|video_begin|> + 13:14 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 15:31 [视频-长播] <|video_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 23:55 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 16:55 [视频-长播] <|video_begin|> + 17:22 [视频-长播] <|video_begin|> + 17:24 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:25 [视频-长播] <|video_begin|> + 23:29 [视频-长播] <|video_begin|> + 23:29 [视频-长播] <|video_begin|> +【2026-02-04】 + 09:49 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 16:49 [视频-点赞/长播] <|video_begin|> + 16:49 [视频-点赞/长播] <|video_begin|> + 16:57 [视频-长播] <|video_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请针对以下主题提取行为逻辑链： +主题：从泛化健康焦虑到具体化保健品探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化健康焦虑到具体化保健品探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化健康焦虑到具体化保健品探索","events":[{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：出现具体的身体部位（膝盖）疼痛问题，并采取了直接的对症处理行为。"},{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"问题泛化：在同一天，关注点从单一的关节问题扩展到另一个中老年男性常见的健康问题（前列腺），表明用户处于对身体多方面健康状况的焦虑期。"},{"date":"2025-11-05","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与方案收敛：在经历了多个具体部位的“被动治疗”后，用户的健康观念发生转变，开始寻求更主动、更根本的解决方案——通过购买明确标有“增强免疫力”的保健品，从“治已病”转向“防未病”。"}]}}<|im_end|> +<|im_start|>user +你是推荐系统助手，需要基于多域时序行为进行证据充分、无目标泄漏的下一交互预测。 +用户多域历史行为（已按时间从早到晚排列）： +1. [较早][直播-关注/评论/点赞/深度观看] <|living_begin|> +2. [较早][直播-关注/评论/点赞/深度观看] <|living_begin|> +3. [较早][直播-关注/评论/深度观看] <|living_begin|> +4. [较早][直播-关注/评论/点赞/深度观看] <|living_begin|> +5. [较早][直播-关注/深度观看] <|living_begin|> +6. [中期][广告-点击] <|ad_begin|> +7. [中期][广告-点击] <|ad_begin|> +8. [中期][广告-点击] <|ad_begin|> +9. [中期][广告-点击] <|ad_begin|> +10. [中期][视频-点赞/转发/完播/长播] <|video_begin|> +11. [中期][视频-点赞/转发/完播/长播] <|video_begin|> +12. [近期][视频-评论/收藏/完播/长播] <|video_begin|> +13. [近期][直播-关注/关注/深度观看，累计2次] <|living_begin|> +14. [近期][视频-点赞/转发/完播/长播] <|video_begin|> +15. [近期][视频-转发/收藏/完播/长播] <|video_begin|> +16. [近期][直播-关注/评论/点赞/深度观看] <|living_begin|> +请推断用户接下来最可能交互的直播，最终只输出一个 itemic token。/no_think<|im_end|> +<|im_start|>assistant +<|living_begin|><|im_end|> +<|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, -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, -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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Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +labels: + +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，挖掘出一条关于“从被动接收到主动探索的健康焦虑演化”的行为链。我需要按照时间顺序，找出那些在场景、兴趣或需求上有递进关系的行为，并最终总结出符合任务要求的逻辑链。 我首先注意到，用户的行为时间线跨度很大，从2025年初一直延续到2026年初。在分析初期，我发现用户的兴趣点非常广泛，从美女/帅哥直播，到文化收藏，再到各种日用品和工具的购买，显得有些分散。然而，当我把目光聚焦在“健康”这个主题上时，一条有趣的线索开始浮现，这条线索并非关于用户自身的健康，而是指向了动物或养殖领域。 我的分析起点是2026年1月30日的一次广告点击行为。在这一天，用户点击了一则关于如何处理小猫软便和饮水问题的广告 <|ad_begin|>。虽然这只是一个简单的点击，但它像一颗石子投入水中，激起了涟漪。我推测，这个广告可能首次将“动物健康”这一具体问题推送到用户面前，触发了他对这类问题的初步认知。这是一种被动的信息接收，可能源于偶然的推送，也可能用户家中确实有宠物或对此类话题有潜在的关注。 顺着这条线索，我回溯了用户的购买历史，试图寻找与此相关的行为。我发现在2025年11月，也就是在点击那则宠物健康广告之前，用户已经有过一些非常专业的购买行为。具体来说，在11月17日，他购买了黄芪多糖 <|prod_begin|>，这是一种常用于提升猪、牛、羊等禽畜免疫力的产品。这个行为让我感到惊讶，因为它远比处理小猫软便要专业得多。这表明用户对动物健康的关注并非一时兴起，而是已经进入了主动寻求解决方案的阶段，并且关注点是更大型的养殖动物，而非普通宠物。 紧接着，在两天后的11月19日，用户的行为进一步印证了我的推测。他购买了一款名为“犊牛快长”的浓缩加强型产品 <|prod_begin|>。这次购买显示出他的需求变得更加具体和聚焦。 + +{"logic_chain":{"name":"从被动接收到主动探索的健康焦虑演化","events":[{"date":"2026-01-30","action":"[广告-点击] <|ad_begin|>","logic":"问题触发：被动接收到关于“宠物软便”和“饮水问题”的广告信息，这构成了用户对动物健康问题的初始认知输入或问题意识的唤醒。"},{"date":"2025-11-17","action":"[商品-购买] <|prod_begin|>","logic":"解决方案探索（免疫调节）：在接触到宠物健康信息后，用户购买了“黄芪多糖”，这是一种常用于提升畜禽免疫力的中兽药成分。行为从泛化的健康知识转向了具体的药物成分购买，显示出用户开始主动寻求解决方案。"},{"date":"2025-11-19","action":"[商品-购买] <|prod_begin|>","logic":"需求聚焦（特定功能）：在购买了普适性的免疫调节产品后，用户的需求进一步聚焦到“犊牛快长”这类更具针对性的生长发育类产品上。这表明用户的关注点从“防病”向“促生长”深化，需求更加具体化。"},{"date":"2025-11-30","action":"[直播-关注] <|living_begin|>","logic":"认知深化与信息渠道升级：在连续购买了两种专业兽药后，用户开始关注专门讲解“林可霉素”、“氟尼辛”等更专业抗生素的“兽药推荐达人”直播。这标志着用户的认知从“买产品”跃迁至“学知识”，主动寻求专业人士的指导，形成了从被动接收信息到主动购买产品，再到寻求专业知识的完整闭环。"}]}}<|im_end|> +{"logic_chain":{"name":"从被动接收信息到主动探索知识的兴趣演化","events":[{"date":"2026-01-26","action":"[广告-深度转化] <|ad_begin|>","logic":"初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"},{"date":"2026-01-29","action":"[视频-转发/长播] <|video_begin|>","logic":"行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|im_end|> +{"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|> +{"logic_chain":{"name":"从泛化健康焦虑到具体化保健品探索","events":[{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：出现具体的身体部位（膝盖）疼痛问题，并采取了直接的对症处理行为。"},{"date":"2024-12-25","action":"[商品-购买] <|prod_begin|>","logic":"问题泛化：在同一天，关注点从单一的关节问题扩展到另一个中老年男性常见的健康问题（前列腺），表明用户处于对身体多方面健康状况的焦虑期。"},{"date":"2025-11-05","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与方案收敛：在经历了多个具体部位的“被动治疗”后，用户的健康观念发生转变，开始寻求更主动、更根本的解决方案——通过购买明确标有“增强免疫力”的保健品，从“治已病”转向“防未病”。"}]}}<|im_end|> +<|living_begin|><|im_end|> + +[INFO|2026-07-16 01:30:07] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-16 01:30:07] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-16 01:30:08,004 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-16 01:30:08,004 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-16 01:30:08,054 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-16 01:30:08,387 >> 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] 2026-07-16 01:30:08,387 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-16 01:30:08] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-16 01:30:08] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-16 01:30:08] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-16 01:30:08] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-16 01:30:08] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-16 01:30:08,524 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-16 01:30:08,524 >> Num examples = 349 +[INFO|trainer.py:1471] 2026-07-16 01:30:08,524 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-16 01:30:08,524 >> Num update steps per epoch = 88 +[INFO|trainer.py:1473] 2026-07-16 01:30:08,524 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-16 01:30:08,524 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-16 01:30:08,524 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-16 01:30:08,524 >> Total optimization steps = 40 +[INFO|trainer.py:1479] 2026-07-16 01:30:08,524 >> Number of trainable parameters = 801,433,600 + 0%| | 0/40 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 40/40 [05:32<00:00, 8.68s/it] 100%|██████████| 40/40 [05:32<00:00, 8.31s/it] +[INFO|trainer.py:3815] 2026-07-16 01:35:40,778 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045 +[INFO|configuration_utils.py:533] 2026-07-16 01:35:40,779 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/config.json +[INFO|configuration_utils.py:816] 2026-07-16 01:35:40,779 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/generation_config.json +{'loss': '2.214', 'grad_norm': '7.688', 'learning_rate': '5.511e-07', 'epoch': '0.1146'} +{'loss': '2.001', 'grad_norm': '6.562', 'learning_rate': '3.494e-07', 'epoch': '0.2292'} +{'loss': '1.967', 'grad_norm': '5.688', 'learning_rate': '1.157e-07', 'epoch': '0.3438'} +{'loss': '1.947', 'grad_norm': '6.406', 'learning_rate': '1.025e-09', 'epoch': '0.4585'} +{'train_runtime': '332.3', 'train_samples_per_second': '0.473', 'train_steps_per_second': '0.12', 'train_loss': '2.032', 'epoch': '0.4585'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-16 01:35:41,767 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-16 01:35:41,791 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/tokenizer_config.json +***** train metrics ***** + epoch = 0.4585 + total_flos = 17691032GF + train_loss = 2.0323 + train_runtime = 0:05:32.25 + train_samples_per_second = 0.473 + train_steps_per_second = 0.12 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v44_v29_clean_live_temporal_lr6e7_ep045/training_loss.png +[WARNING|2026-07-16 01:35:41] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-16 01:35:41] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-16 01:35:41,890 >> 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..6a325a1 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.4584527220630373, + "total_flos": 1.8995601921736704e+16, + "train_loss": 2.0322839260101317, + "train_runtime": 332.2523, + "train_samples_per_second": 0.473, + "train_steps_per_second": 0.12 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..2e64c93 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,5 @@ +{"current_steps": 10, "total_steps": 40, "loss": 2.213998794555664, "lr": 5.511499434787585e-07, "epoch": 0.11461318051575932, "percentage": 25.0, "elapsed_time": "0:01:21", "remaining_time": "0:04:05"} +{"current_steps": 20, "total_steps": 40, "loss": 2.0011642456054686, "lr": 3.493783770842202e-07, "epoch": 0.22922636103151864, "percentage": 50.0, "elapsed_time": "0:02:43", "remaining_time": "0:02:43"} +{"current_steps": 30, "total_steps": 40, "loss": 1.9670379638671875, "lr": 1.1573618619309965e-07, "epoch": 0.3438395415472779, "percentage": 75.0, "elapsed_time": "0:04:09", "remaining_time": "0:01:23"} +{"current_steps": 40, "total_steps": 40, "loss": 1.946934700012207, "lr": 1.0246520979990458e-09, "epoch": 0.4584527220630373, "percentage": 100.0, "elapsed_time": "0:05:32", "remaining_time": "0:00:00"} +{"current_steps": 40, "total_steps": 40, "epoch": 0.4584527220630373, "percentage": 100.0, "elapsed_time": "0:05:32", "remaining_time": 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