commit 382dfc90219306d5e7ad420e0c371a4829e83503 Author: ModelHub XC Date: Sun Jul 19 04:14:10 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: lldois/v37_v29_r0_perception_lr6e7_ep10 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs 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100644 index 0000000..f2500df --- /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: v37_v29_r0_perception_lr6e7_ep10 + results: [] +--- + + + +# v37_v29_r0_perception_lr6e7_ep10 + +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_v37_r0_perception 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: 202607187 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 1.0 + +### 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..da8954b --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 2.1268932852271104e+17, + "train_loss": 1.9281885245235708, + "train_runtime": 3538.1073, + "train_samples_per_second": 0.505, + "train_steps_per_second": 0.126 +} \ 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..aa30f64 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,72 @@ +{ + "created_at": "2026-07-15 00:59:15", + "name": "v37_v29_r0_perception_lr6e7_ep10", + "base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v37_r0_perception", + "dataset_manifest": { + "name": "v37_r0_perception", + "path": "/home/ll/llm4rec/experiments/data/v37_r0_perception.jsonl", + "records": 80000, + "bytes": 267058830, + "sha256": "71579dd6021ab3045524e80275a793d3adcb1b552884209837e77c149d880266", + "requested_components": { + "general": 24000, + "r0": 28000, + "r3_think": 8000, + "r3_direct": 8000, + "old_rec": 4000, + "old_user": 4000, + "old_item": 4000 + }, + "groups": { + "r3": 16000, + "general": 24000, + "r0": 28000, + "old_item": 4000, + "old_rec": 4000, + "old_user": 4000 + }, + "variants": { + "profile_video/ad_direct": 1832, + "official_general_direct": 12089, + "sid_to_caption_direct": 6995, + "official_general_cot": 11911, + "sid_to_caption_cot": 6897, + "original_competition": 5843, + "caption_to_sid_direct": 14108, + "profile_live_cot": 1864, + "profile_video/video_direct": 2451, + "rec_no_think_direct_final": 2009, + "user_strict_logic": 556, + "profile_goods_direct": 1859, + "profile_video/ad_cot": 1818, + "user_logic_json_strict": 573, + "profile_video/video_cot": 2471, + "item_no_think_direct_final": 721, + "item_short_think": 712, + "user_strict_array": 660, + "profile_goods_cot": 1847, + "profile_live_direct": 1858, + "user_array_json_strict": 653, + "user_extra_no_think_logic": 273 + }, + "routes": { + "no_think": 48386, + "think": 31614 + }, + "target_leakage": 0, + "seed": 202607154 + }, + "learning_rate": "6.0e-7", + "epochs": 1.0, + "stages": null, + "note": "R0-heavy bidirectional caption/SID grounding to improve item perception and downstream recommendation semantics.", + "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_v34_v43.py", + "script_sha256": "fbf01773c61579cf6159f64442fc0c8902714c84ed23b488bc9a6236a154f402", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v37_v29_r0_perception_lr6e7_ep10.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_v34_v43.py --single v37_v29_r0_perception_lr6e7_ep10 --gpu " +} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..8b8e592 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d57eacf720283361a9370f87b775077118b51c3ee0c454087508855abbcd62b +size 1602902832 diff --git a/runs/Jul15_02-03-01_ubuntu-System-Product-Name/events.out.tfevents.1784052181.ubuntu-System-Product-Name.1381877.0 b/runs/Jul15_02-03-01_ubuntu-System-Product-Name/events.out.tfevents.1784052181.ubuntu-System-Product-Name.1381877.0 new file mode 100644 index 0000000..db09761 --- /dev/null +++ b/runs/Jul15_02-03-01_ubuntu-System-Product-Name/events.out.tfevents.1784052181.ubuntu-System-Product-Name.1381877.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1aa8f0e8404d4245df9acfb651b6274466f519fcd0c6daf76b177b00d3fc83b5 +size 15538 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..01859ac --- /dev/null +++ b/train.log @@ -0,0 +1,2225 @@ +[2026-07-15 02:02:30] stage 1/1: /home/ll/llm4rec/experiments/configs/v37_v29_r0_perception_lr6e7_ep10.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|2026-07-15 02:02:33] 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] 2026-07-15 02:02:33,862 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 02:02:33,864 >> 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] 2026-07-15 02:02:34,637 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 02:02:34,638 >> 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] 2026-07-15 02:02:34,638 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 02:02:34,639 >> 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|2026-07-15 02:02:35] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v37_r0_perception.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: 25091 examples [00:00, 220078.38 examples/s] Generating train split: 50108 examples [00:00, 235506.56 examples/s] Generating train split: 80000 examples [00:00, 243823.78 examples/s] Generating train split: 80000 examples [00:00, 239817.38 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/80000 [00:00user +【用户交互历史】： +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [直播-关注] <|living_begin|> +【2025-05-06】 + --:-- [直播-关注] <|living_begin|> +【2025-05-14】 + --:-- [直播-关注] <|living_begin|> +【2025-05-31】 + --:-- [直播-关注] <|living_begin|> +【2025-06-05】 + --:-- [直播-关注] <|living_begin|> +【2025-06-10】 + --:-- [直播-关注] <|living_begin|> +【2025-06-11】 + --:-- [直播-关注] <|living_begin|> +【2025-06-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注] <|living_begin|> +【2025-07-06】 + --:-- [直播-关注] <|living_begin|> +【2025-07-12】 + --:-- [直播-关注] <|living_begin|> +【2025-07-13】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + 13:31 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:01 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:09 [广告-深度转化] <|ad_begin|> + 08:12 [广告-深度转化] <|ad_begin|> + 08:18 [广告-深度转化] <|ad_begin|> + 08:55 [广告-深度转化] <|ad_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:19 [广告-深度转化] <|ad_begin|> + 07:59 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 09:42 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 08:39 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 07:23 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 09:50 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:34 [广告-深度转化] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:31 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 08:46 [广告-深度转化] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 23:26 [广告-深度转化] <|ad_begin|> + 23:27 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:21 [广告-深度转化] <|ad_begin|> + 03:25 [广告-深度转化] <|ad_begin|> + 03:27 [广告-深度转化] <|ad_begin|> + 08:36 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:21 [广告-深度转化] <|ad_begin|> + 02:31 [广告-深度转化] <|ad_begin|> + 05:15 [广告-深度转化] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 10:44 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:20 [广告-点击] <|ad_begin|> + 03:24 [广告-深度转化] <|ad_begin|> + 03:24 [广告-点击] <|ad_begin|> + 08:34 [广告-点击] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> + 23:48 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:24 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 09:14 [广告-深度转化] <|ad_begin|> + 09:14 [广告-点击] <|ad_begin|> + 09:46 [广告-点击] <|ad_begin|> + 10:35 [广告-点击] <|ad_begin|> + 10:44 [广告-点击] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 23:31 [广告-点击] <|ad_begin|> + 23:39 [广告-点击] <|ad_begin|> + 23:43 [广告-点击] <|ad_begin|> + 23:44 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:21 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 02:16 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:06 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-11】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:41 [广告-点击] <|ad_begin|> + 13:50 [广告-深度转化] <|ad_begin|> + 13:50 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 00:31 [广告-深度转化] <|ad_begin|> + 00:31 [广告-点击] <|ad_begin|> + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:24 [广告-深度转化] <|ad_begin|> + 07:24 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 02:56 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 23:18 [广告-点击] <|ad_begin|> +【2026-01-20】 + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 10:18 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:21 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 00:51 [视频-收藏/长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 10:15 [视频-转发/长播] <|video_begin|> + 10:42 [视频-收藏/长播] <|video_begin|> + 10:51 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-关注/长播] <|video_begin|> + 12:32 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:32 [广告-深度转化] <|ad_begin|> + 04:32 [广告-点击] <|ad_begin|> + 04:48 [视频-点赞/长播] <|video_begin|> + 06:17 [视频-点赞/长播] <|video_begin|> + 06:23 [视频-评论/长播] <|video_begin|> + 22:15 [视频-浏览] <|video_begin|> +【2026-01-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:20 [广告-深度转化] <|ad_begin|> + 01:20 [广告-点击] <|ad_begin|> + 21:51 [广告-深度转化] <|ad_begin|> + 21:51 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:33 [广告-点击] <|ad_begin|> + 08:05 [视频-转发/长播] <|video_begin|> + 11:18 [视频-转发/长播] <|video_begin|> + 11:28 [视频-点赞/长播] <|video_begin|> + 22:05 [视频-收藏/长播] <|video_begin|> +【2026-01-26】 + 00:37 [视频-点赞/长播] <|video_begin|> + 12:18 [视频-收藏/长播] <|video_begin|> + 12:21 [视频-收藏/长播] <|video_begin|> + 12:46 [视频-转发/收藏/长播] <|video_begin|> + 13:58 [视频-收藏/长播] <|video_begin|> + 22:21 [视频-收藏/长播] <|video_begin|> + 23:08 [视频-收藏/长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:19 [广告-深度转化] <|ad_begin|> + 01:19 [广告-点击] <|ad_begin|> + 02:46 [视频-评论/长播] <|video_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> + 11:44 [视频-转发/长播] <|video_begin|> + 21:51 [视频-点赞/长播] <|video_begin|> + 22:17 [视频-收藏/长播] <|video_begin|> + 23:56 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:34 [视频-收藏/长播] <|video_begin|> + 12:39 [广告-点击] <|ad_begin|> + 22:09 [广告-深度转化] <|ad_begin|> + 22:09 [广告-点击] <|ad_begin|> + 22:15 [广告-深度转化] <|ad_begin|> + 22:15 [广告-点击] <|ad_begin|> + 23:16 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 09:29 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 08:06 [广告-深度转化] <|ad_begin|> + 08:06 [广告-点击] <|ad_begin|> + 08:38 [视频-评论/长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + 00:00 [广告-深度转化] <|ad_begin|> + 00:00 [广告-点击] <|ad_begin|> + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 09:46 [视频-关注/长播] <|video_begin|> + 12:12 [视频-点赞/长播] <|video_begin|> + 12:19 [视频-评论/长播] <|video_begin|> + 13:21 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 10:17 [视频-转发/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 01:52 [广告-深度转化] <|ad_begin|> + 01:52 [广告-点击] <|ad_begin|> + 03:03 [广告-深度转化] <|ad_begin|> + 03:03 [广告-点击] <|ad_begin|> + 04:31 [视频-评论/长播] <|video_begin|> + 07:25 [视频-评论/长播] <|video_begin|> + 11:50 [视频-收藏/长播] <|video_begin|> + 12:14 [广告-点击] <|ad_begin|> + 13:29 [视频-转发/长播] <|video_begin|> + 13:34 [广告-点击] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> + 23:08 [视频-转发/长播] <|video_begin|> +【2026-02-03】 + 01:35 [广告-点击] <|ad_begin|> + 11:45 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 22:17 [视频-关注/长播] <|video_begin|> + 22:37 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:34 [视频-收藏/长播] <|video_begin|> + 08:39 [视频-关注/评论/长播] <|video_begin|> + 21:56 [视频-收藏/长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:21 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 04:39 [广告-深度转化] <|ad_begin|> + 04:39 [广告-点击] <|ad_begin|> + 05:11 [视频-长播] <|video_begin|> + 05:52 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 07:58 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:58 [视频-长播] <|video_begin|> + 11:43 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 11:58 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 14:01 [视频-关注/长播] <|video_begin|> + 14:04 [视频-关注/长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-关注/长播] <|video_begin|> + 14:10 [视频-长播] <|video_begin|> + 14:10 [视频-关注/长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:21 [视频-长播] <|video_begin|> + 14:21 [视频-关注/长播] <|video_begin|> + 14:22 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题提炼具有递进逻辑与内部关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请面向以下主题提取行为逻辑链： +主题：从泛化兴趣到专业化聚焦的收藏品认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化兴趣到专业化聚焦的收藏品认知演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化兴趣到专业化聚焦的收藏品认知演化", "events": [{"date": "2025-12-17", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始触发：被动接收大量泛化的“老物件上门回收”广告，形成初步认知。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣聚焦：从泛化的“老物件”概念，收敛到对特定品类“古钱币”的价值和历史产生主动兴趣，并参与评论互动。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知跃迁：兴趣从“物品本身”深化至“专业交易场景”，关注点转向“拍卖会”这一专业变现或投资渠道，完成了从了解到实践的认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-03-02】 + --:-- [直播-关注] <|living_begin|> +【2025-03-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> +【2025-04-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [直播-关注] <|living_begin|> +【2025-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [直播-关注] <|living_begin|> +【2025-05-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-22】 + --:-- [直播-关注] <|living_begin|> +【2025-05-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-06】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + 04:54 [广告-点击] <|ad_begin|> +【2025-12-10】 + 14:33 [广告-点击] <|ad_begin|> +【2025-12-11】 + 04:54 [广告-点击] <|ad_begin|> +【2025-12-13】 + 02:25 [广告-点击] <|ad_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + 16:06 [广告-点击] <|ad_begin|> +【2025-12-19】 + 04:32 [广告-点击] <|ad_begin|> + 09:12 [广告-点击] <|ad_begin|> +【2025-12-26】 + 10:05 [广告-点击] <|ad_begin|> +【2025-12-28】 + --:-- [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-购买] <|prod_begin|> +【2025-12-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-01】 + 04:34 [广告-点击] <|ad_begin|> +【2026-01-02】 + 15:34 [广告-点击] <|ad_begin|> + 16:57 [广告-点击] <|ad_begin|> +【2026-01-06】 + 13:40 [广告-点击] <|ad_begin|> +【2026-01-11】 + 02:05 [广告-点击] <|ad_begin|> + 02:09 [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|> +【2026-01-12】 + 02:09 [广告-点击] <|ad_begin|> +【2026-01-13】 + 01:44 [广告-点击] <|ad_begin|> + 04:10 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:03 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + 01:30 [广告-点击] <|ad_begin|> + 01:32 [广告-点击] <|ad_begin|> + 04:18 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:31 [广告-点击] <|ad_begin|> +【2026-01-18】 + 10:31 [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|> +【2026-01-19】 + 15:51 [广告-点击] <|ad_begin|> +【2026-01-20】 + 10:39 [广告-点击] <|ad_begin|> + 15:21 [广告-点击] <|ad_begin|> +【2026-01-21】 + 09:37 [广告-点击] <|ad_begin|> +【2026-01-22】 + 10:19 [视频-点赞/长播] <|video_begin|> + 10:19 [视频-点赞/长播] <|video_begin|> + 10:27 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:41 [视频-点赞/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + 10:49 [视频-点赞/长播] <|video_begin|> + 14:56 [视频-点赞/长播] <|video_begin|> + 14:59 [视频-点赞/长播] <|video_begin|> + 21:23 [广告-点击] <|ad_begin|> +【2026-01-23】 + 05:04 [视频-点赞/长播] <|video_begin|> + 05:25 [视频-评论/点赞/长播] <|video_begin|> + 05:30 [视频-点赞/长播] <|video_begin|> + 14:06 [视频-评论/点赞/长播] <|video_begin|> + 14:58 [广告-点击] <|ad_begin|> +【2026-01-24】 + 04:26 [视频-点赞/长播] <|video_begin|> + 08:12 [视频-点赞/长播] <|video_begin|> + 08:17 [视频-评论/点赞/长播] <|video_begin|> + 08:19 [视频-评论/长播] <|video_begin|> + 19:10 [广告-点击] <|ad_begin|> +【2026-01-25】 + 06:30 [视频-点赞/长播] <|video_begin|> + 08:48 [视频-点赞/长播] <|video_begin|> + 09:15 [视频-点赞/长播] <|video_begin|> + 10:31 [视频-点赞/长播] <|video_begin|> + 11:23 [视频-点赞/长播] <|video_begin|> + 11:42 [广告-点击] <|ad_begin|> + 15:53 [视频-评论/点赞/长播] <|video_begin|> + 16:58 [视频-评论/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-购买] <|prod_begin|> +【2026-01-26】 + 17:45 [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-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + 04:15 [视频-评论/长播] <|video_begin|> + 04:55 [视频-点赞/长播] <|video_begin|> + 05:09 [视频-点赞/长播] <|video_begin|> + 05:37 [广告-点击] <|ad_begin|> + 13:52 [视频-评论/收藏/点赞/长播] <|video_begin|> + 14:17 [视频-点赞/长播] <|video_begin|> + 15:50 [广告-点击] <|ad_begin|> +【2026-01-28】 + 05:11 [视频-点赞/长播] <|video_begin|> + 05:32 [视频-点赞/长播] <|video_begin|> + 08:54 [视频-收藏/点赞/长播] <|video_begin|> + 08:54 [视频-收藏/点赞/长播] <|video_begin|> + 09:17 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 02:00 [视频-收藏/点赞/长播] <|video_begin|> + 02:17 [视频-收藏/点赞/长播] <|video_begin|> + 06:00 [广告-点击] <|ad_begin|> + 08:23 [视频-评论/长播] <|video_begin|> + 10:13 [视频-收藏/点赞/长播] <|video_begin|> + 10:13 [视频-点赞/长播] <|video_begin|> + 10:21 [广告-点击] <|ad_begin|> + 13:20 [视频-收藏/点赞/长播] <|video_begin|> + 14:36 [广告-点击] <|ad_begin|> + 15:29 [视频-收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-30】 + 02:10 [广告-点击] <|ad_begin|> + 02:39 [视频-收藏/点赞/长播] <|video_begin|> + 04:49 [视频-点赞/长播] <|video_begin|> + 05:06 [视频-点赞/长播] <|video_begin|> + 12:14 [视频-点赞/长播] <|video_begin|> + 12:37 [视频-收藏/点赞/长播] <|video_begin|> + 12:37 [视频-评论/长播] <|video_begin|> + 18:06 [视频-收藏/点赞/长播] <|video_begin|> + 18:30 [视频-收藏/点赞/长播] <|video_begin|> + 18:33 [视频-评论/点赞/长播] <|video_begin|> + 18:33 [视频-评论/点赞/长播] <|video_begin|> + 18:34 [视频-点赞/长播] <|video_begin|> + 18:56 [视频-收藏/点赞/长播] <|video_begin|> + 18:56 [视频-收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:21 [广告-点击] <|ad_begin|> +【2026-01-31】 + 02:14 [视频-收藏/点赞/长播] <|video_begin|> + 12:04 [视频-收藏/点赞/长播] <|video_begin|> + 17:12 [视频-收藏/点赞/长播] <|video_begin|> + 18:33 [视频-收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + 00:11 [视频-点赞/长播] <|video_begin|> + 03:55 [广告-点击] <|ad_begin|> + 06:54 [视频-点赞/长播] <|video_begin|> + 06:54 [视频-评论/收藏/点赞/长播] <|video_begin|> + 08:58 [视频-收藏/点赞/长播] <|video_begin|> + 09:33 [视频-收藏/点赞/长播] <|video_begin|> + 11:15 [视频-点赞/长播] <|video_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + 12:56 [视频-点赞/长播] <|video_begin|> + 12:59 [视频-评论/点赞/长播] <|video_begin|> + 13:26 [视频-收藏/点赞/长播] <|video_begin|> + 16:42 [视频-收藏/点赞/长播] <|video_begin|> + 17:04 [视频-点赞/长播] <|video_begin|> + 17:04 [视频-点赞/长播] <|video_begin|> + 17:04 [视频-点赞/长播] <|video_begin|> + 17:17 [视频-点赞/长播] <|video_begin|> + 17:22 [视频-收藏/长播] <|video_begin|> + 18:23 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-02】 + 00:57 [广告-点击] <|ad_begin|> + 02:39 [广告-点击] <|ad_begin|> + 03:59 [视频-收藏/点赞/长播] <|video_begin|> + 05:21 [视频-收藏/点赞/长播] <|video_begin|> + 05:25 [视频-收藏/点赞/长播] <|video_begin|> + 05:57 [视频-评论/长播] <|video_begin|> + 07:06 [视频-收藏/点赞/长播] <|video_begin|> + 07:06 [视频-收藏/点赞/长播] <|video_begin|> + 07:24 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-03】 + 10:56 [视频-点赞/长播] <|video_begin|> + 11:01 [广告-点击] <|ad_begin|> + 15:08 [视频-点赞/长播] <|video_begin|> + 15:48 [广告-点击] <|ad_begin|> + 16:39 [视频-收藏/点赞/长播] <|video_begin|> + 16:39 [视频-点赞/长播] <|video_begin|> + 16:44 [视频-点赞/长播] <|video_begin|> + 16:53 [视频-收藏/点赞/长播] <|video_begin|> + 16:56 [视频-点赞/长播] <|video_begin|> + 17:03 [视频-点赞/长播] <|video_begin|> + 17:27 [视频-点赞/长播] <|video_begin|> + 18:17 [视频-收藏/点赞/长播] <|video_begin|> + 18:21 [视频-收藏/点赞/长播] <|video_begin|> + 18:50 [视频-点赞/长播] <|video_begin|> + 18:59 [视频-点赞/长播] <|video_begin|> + 19:54 [视频-评论/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-04】 + 05:27 [视频-收藏/点赞/长播] <|video_begin|> + 05:44 [视频-收藏/点赞/长播] <|video_begin|> + 06:41 [视频-收藏/点赞/长播] <|video_begin|> + 08:54 [视频-收藏/点赞/长播] <|video_begin|> + 08:55 [视频-收藏/点赞/长播] <|video_begin|> + 09:00 [视频-收藏/点赞/长播] <|video_begin|> + 13:56 [广告-点击] <|ad_begin|> + 14:04 [视频-收藏/点赞/长播] <|video_begin|> + 17:14 [视频-收藏/点赞/长播] <|video_begin|> + 17:26 [视频-评论/长播] <|video_begin|> + 17:30 [视频-评论/长播] <|video_begin|> + 17:43 [广告-点击] <|ad_begin|> + 17:43 [视频-评论/长播] <|video_begin|> + 17:50 [视频-评论/长播] <|video_begin|> + 19:23 [视频-收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:54 [视频-收藏/点赞/长播] <|video_begin|> + 23:04 [视频-收藏/点赞/长播] <|video_begin|> + 23:09 [视频-收藏/点赞/长播] <|video_begin|> + 23:10 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-05】 + 07:17 [视频-收藏/点赞/长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 07:42 [视频-长播] <|video_begin|> + 09:55 [视频-长播] <|video_begin|> + 09:55 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:03 [视频-收藏/点赞/长播] <|video_begin|> + 10:11 [视频-长播] <|video_begin|> + 10:13 [视频-长播] <|video_begin|> + 10:22 [视频-长播] <|video_begin|> + 10:22 [视频-长播] <|video_begin|> + 10:28 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:40 [视频-长播] <|video_begin|> + 10:40 [视频-长播] <|video_begin|> + 10:40 [视频-长播] <|video_begin|> + 10:40 [视频-长播] <|video_begin|> + 10:45 [视频-长播] <|video_begin|> + 10:45 [视频-收藏/点赞/长播] <|video_begin|> + 10:48 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:51 [视频-长播] <|video_begin|> + 10:51 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:03 [视频-长播] <|video_begin|> + 11:11 [视频-长播] <|video_begin|> + 11:11 [视频-长播] <|video_begin|> + 11:11 [视频-长播] <|video_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:05 [视频-长播] <|video_begin|> + 12:09 [视频-长播] <|video_begin|> + 12:09 [视频-收藏/点赞/长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 12:20 [视频-收藏/点赞/长播] <|video_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:28 [视频-长播] <|video_begin|> + 12:28 [视频-长播] <|video_begin|> + 12:29 [视频-长播] <|video_begin|> + 12:29 [视频-长播] <|video_begin|> + 12:29 [视频-长播] <|video_begin|> + 12:36 [视频-长播] <|video_begin|> + 12:36 [视频-长播] <|video_begin|> + 12:48 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 13:12 [视频-长播] <|video_begin|> + 13:12 [视频-长播] <|video_begin|> + 13:12 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:27 [视频-点赞/长播] <|video_begin|> + 14:07 [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）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心提取逻辑 (递进性准则)： +按时间顺序提取 5 步以内的行为链，要求后项交互B必须是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是因为 A 才产生的延伸需求，严禁提取任何品类平级、内容重复或仅因天气/促销产生的凑单并列行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 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：从被动游戏广告触达到主动探索游戏世界观与角色设定的兴趣深化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动游戏广告触达到主动探索游戏世界观与角色设定的兴趣深化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/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游戏广告到主动探索游戏世界观，并最终深化为对角色设定产生浓厚兴趣的行为链。 + +我首先注意到，用户的早期行为主要集中在饲养宠物（特别是金丝熊）和购买一些生活用品上，比如从三月初开始，他密集地购买了金丝熊活体 <|prod_begin|>、<|prod_begin|> 以及大量的饲料、垫料和玩具。这表明他当时的核心兴趣是宠物饲养。同时，他也关注了一些手工文创和颜值才艺类直播，展现出对可爱、治愈和互动内容的偏好。 + +直到2025年12月，用户的行为模式开始出现一个显著的转折点。在12月13日，我观察到他首次点击了一则关于《超自然行动组》的游戏广告 <|ad_begin|>。这则广告具体展示了角色“羽子”的装备、任务和战斗过程，这似乎是他与该游戏IP的初次接触，是一次被动的、由广告驱动的兴趣触发。在此之前，他的游戏相关行为并不突出，这次点击可以看作是一颗兴趣的种子。 + +这颗种子在接下来的一个多月里开始萌芽。到了2026年1月22日，我发现他不再仅仅是被动地看广告，而是主动点赞并长时间观看了一个关于“超自然行动组”闺蜜游戏日常的视频 <|video_begin|>。这个行为非常关键，它标志着用户的兴趣从对广告所展示的“玩法”的初步认知，深化到了对游戏“日常趣味性”和角色个性的主动关注。这说明广告内容成功地转化为了他的主动探索行为，他对这个游戏世界的兴趣正在加深。 + +紧接着，在1月24日，我看到了一个更有趣的现象。他点赞了一个深度解析《第五人格》梦之女巫角色设计的视频 <|video_begin|>。这个视频探讨了角色的神话灵感、设计演变等深层内容。这对我来说是一个强烈的信号，表明他的兴趣已经从单纯的游戏内容消费，跃迁到了对游戏角色如何被创造、其背后的世界观和美学构建的探究层面。更重要的是，他的兴趣范围从《超自然行动组》横向拓展到了风格相似但设计不同的《第五人格》，这说明他已经形成了一个更为抽象的偏好——对“游戏角色设计”本身的浓厚兴趣，而不仅仅局限于某一款游戏。 + +最终，在1月29日，我发现他点击了一款《超自然行动》的二次元周边相框商品 <|prod_begin|>。这一步可以说是整个兴趣链的闭环。用户的关注点从纯粹的线上数字内容，延伸到了希望在现实生活中拥有实体周边。这不仅代表了他对该游戏IP的高度认同，也表明他希望将这种虚拟世界的喜爱转化为一种物理的、可触摸的收藏。这是粉丝身份认同的典型表现，也是兴趣深度和粘性的最终体现。 + +综上所述，我推断出一条清晰的兴趣深化路径：用户最初因一次广告点击 <|ad_begin|> 对《超自然行动组》产生初步认知，随后通过主动观看相关的趣味日常视频 <|video_begin|> 加深了兴趣。接着，他的关注点升华，开始探索更深层次的角色设计理念，甚至将这种兴趣拓展到了同品类的其他游戏 <|video_begin|>。最终，这种强烈的兴趣促使他关注并点击了相关的实体周边商品 <|prod_begin|>，完成了从被动触达到主动探索，再到渴望拥有的完整行为闭环。未来，他有较大概率会被类似的游戏角色设计解析、世界观探讨以及相关的实体或虚拟周边商品所吸引。 +{"logic_chain": {"name": "从被动游戏广告触达到主动探索游戏世界观与角色设定的兴趣深化", "events": [{"date": "2025-12-13", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收并点击了特定游戏《超自然行动组》的角色及玩法广告，建立了初步认知。"}, {"date": "2026-01-22", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "兴趣确认与加深：从广告的单向灌输，转变为主动观看并点赞与该游戏相关的二次创作内容，关注点从“玩法展示”转向了“游戏日常/趣味性”，交互深度增加。"}, {"date": "2026-01-24", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "认知跃迁与横向拓展：兴趣从游戏日常的表层内容，跃迁至对游戏角色设计的深度解析（如设计灵感、演变过程），体现了对游戏世界观和美学构建的探究。同时，兴趣从《超自然行动组》拓展到同品类但有不同设计风格的《第五人格》，表明用户已形成对“角色设计”这一抽象概念的关注。"}, {"date": "2026-01-29", "action": "[商品-点击] <|prod_begin|>", "logic": "行为闭环：兴趣从线上的内容消费，延伸至对线下实体周边的关注，形成了从“了解到探索再到拥有”的完整兴趣闭环，是认知深度和粉丝身份认同的最终体现。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-06】 + --:-- [直播-关注] <|living_begin|> +【2025-09-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [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|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + 02:55 [广告-深度转化] <|ad_begin|> + 13:21 [广告-深度转化] <|ad_begin|> +【2025-12-09】 + 14:00 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + 22:11 [广告-深度转化] <|ad_begin|> +【2025-12-11】 + 20:35 [广告-深度转化] <|ad_begin|> +【2025-12-13】 + 02:48 [广告-深度转化] <|ad_begin|> + 05:50 [广告-深度转化] <|ad_begin|> + 07:38 [广告-深度转化] <|ad_begin|> + 09:05 [广告-深度转化] <|ad_begin|> + 10:58 [广告-深度转化] <|ad_begin|> + 10:58 [广告-点击] <|ad_begin|> + 21:15 [广告-点击] <|ad_begin|> +【2025-12-14】 + 07:14 [广告-点击] <|ad_begin|> + 08:31 [广告-深度转化] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 21:55 [广告-深度转化] <|ad_begin|> + 21:55 [广告-点击] <|ad_begin|> +【2025-12-17】 + 21:15 [广告-点击] <|ad_begin|> +【2025-12-19】 + 22:00 [广告-点击] <|ad_begin|> +【2025-12-21】 + 05:53 [广告-深度转化] <|ad_begin|> + 05:53 [广告-点击] <|ad_begin|> + 06:45 [广告-深度转化] <|ad_begin|> + 06:45 [广告-点击] <|ad_begin|> +【2025-12-22】 + 06:39 [广告-点击] <|ad_begin|> + 22:55 [广告-深度转化] <|ad_begin|> + 22:55 [广告-点击] <|ad_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> + 11:26 [广告-深度转化] <|ad_begin|> + 11:26 [广告-点击] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> +【2025-12-24】 + 06:01 [广告-深度转化] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> + 07:58 [广告-深度转化] <|ad_begin|> + 07:58 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-购买] <|prod_begin|> + 23:38 [广告-深度转化] <|ad_begin|> + 23:38 [广告-点击] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:25 [广告-深度转化] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + 05:46 [广告-深度转化] <|ad_begin|> + 05:46 [广告-点击] <|ad_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + 05:22 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:07 [广告-点击] <|ad_begin|> + 07:05 [广告-深度转化] <|ad_begin|> + 07:05 [广告-点击] <|ad_begin|> +【2026-01-03】 + 00:03 [广告-深度转化] <|ad_begin|> + 00:03 [广告-点击] <|ad_begin|> +【2026-01-05】 + 06:20 [广告-深度转化] <|ad_begin|> + 06:20 [广告-点击] <|ad_begin|> + 10:20 [广告-深度转化] <|ad_begin|> + 10:20 [广告-点击] <|ad_begin|> + 11:48 [广告-深度转化] <|ad_begin|> + 11:48 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> + 10:06 [广告-深度转化] <|ad_begin|> + 10:06 [广告-点击] <|ad_begin|> + 23:59 [广告-深度转化] <|ad_begin|> + 23:59 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 21:53 [广告-点击] <|ad_begin|> + 22:18 [广告-深度转化] <|ad_begin|> + 22:18 [广告-点击] <|ad_begin|> +【2026-01-08】 + 02:07 [广告-深度转化] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + 02:51 [广告-深度转化] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 11:22 [广告-点击] <|ad_begin|> +【2026-01-09】 + 08:44 [广告-深度转化] <|ad_begin|> + 08:44 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> + 02:08 [广告-深度转化] <|ad_begin|> + 02:08 [广告-点击] <|ad_begin|> + 08:00 [广告-深度转化] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> +【2026-01-13】 + 10:53 [广告-深度转化] <|ad_begin|> + 10:53 [广告-点击] <|ad_begin|> + 18:19 [广告-深度转化] <|ad_begin|> + 18:19 [广告-点击] <|ad_begin|> + 21:48 [广告-点击] <|ad_begin|> +【2026-01-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:51 [广告-深度转化] <|ad_begin|> + 05:51 [广告-点击] <|ad_begin|> + 07:45 [广告-深度转化] <|ad_begin|> + 07:45 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-17】 + 05:20 [广告-点击] <|ad_begin|> + 09:59 [广告-深度转化] <|ad_begin|> + 09:59 [广告-点击] <|ad_begin|> + 11:21 [广告-深度转化] <|ad_begin|> + 11:21 [广告-点击] <|ad_begin|> +【2026-01-18】 + 00:41 [广告-深度转化] <|ad_begin|> + 00:41 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 12:41 [广告-深度转化] <|ad_begin|> + 12:41 [广告-点击] <|ad_begin|> + 16:10 [广告-点击] <|ad_begin|> + 17:10 [广告-点击] <|ad_begin|> +【2026-01-20】 + 08:17 [广告-深度转化] <|ad_begin|> + 08:17 [广告-点击] <|ad_begin|> +【2026-01-21】 + 06:32 [广告-深度转化] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> +【2026-01-22】 + 01:26 [视频-点赞/长播] <|video_begin|> + 01:49 [视频-收藏/长播] <|video_begin|> + 10:10 [广告-深度转化] <|ad_begin|> + 10:10 [广告-点击] <|ad_begin|> + 21:37 [视频-评论/长播] <|video_begin|> + 21:48 [视频-关注/长播] <|video_begin|> +【2026-01-23】 + 10:17 [广告-点击] <|ad_begin|> + 12:21 [视频-转发/长播] <|video_begin|> + 21:40 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 05:12 [广告-深度转化] <|ad_begin|> + 05:12 [广告-点击] <|ad_begin|> + 23:14 [广告-点击] <|ad_begin|> + 23:33 [广告-深度转化] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-01-26】 + 04:50 [视频-评论/长播] <|video_begin|> +【2026-01-27】 + 00:17 [视频-关注/长播] <|video_begin|> + 00:18 [广告-点击] <|ad_begin|> + 04:50 [广告-深度转化] <|ad_begin|> + 04:50 [广告-点击] <|ad_begin|> + 21:38 [视频-浏览] <|video_begin|> +【2026-01-28】 + 23:48 [广告-深度转化] <|ad_begin|> + 23:48 [广告-点击] <|ad_begin|> +【2026-01-29】 + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 07:15 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [商品-购买] <|prod_begin|> + 10:08 [视频-收藏/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-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|> +【2026-02-01】 + --:-- [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|> + 20:51 [视频-评论/长播] <|video_begin|> + 21:09 [广告-深度转化] <|ad_begin|> + 21:09 [广告-点击] <|ad_begin|> + 21:22 [广告-深度转化] <|ad_begin|> + 21:22 [广告-点击] <|ad_begin|> +【2026-02-02】 + --:-- [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|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:04 [视频-评论/长播] <|video_begin|> +【2026-02-04】 + 02:00 [视频-长播] <|video_begin|> + 02:00 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:18 [广告-点击] <|ad_begin|> + 02:21 [视频-长播] <|video_begin|> + 02:23 [视频-长播] <|video_begin|> + 02:23 [视频-长播] <|video_begin|> + 02:36 [视频-长播] <|video_begin|> + 09:44 [视频-长播] <|video_begin|> + 09:44 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:42 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:49 [视频-长播] <|video_begin|> + 12:49 [视频-长播] <|video_begin|> + 13:13 [视频-长播] <|video_begin|> + 13:13 [视频-长播] <|video_begin|> + 13:28 [视频-长播] <|video_begin|> + 13:28 [视频-长播] <|video_begin|> + 13:28 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:39 [视频-长播] <|video_begin|> + 13:46 [视频-长播] <|video_begin|> + 22:43 [视频-长播] <|video_begin|> +【2026-02-05】 + 01:38 [视频-长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:22 [视频-长播] <|video_begin|> + 02:22 [视频-长播] <|video_begin|> + 03:15 [视频-长播] <|video_begin|> + 03:28 [视频-长播] <|video_begin|> + 04:11 [视频-长播] <|video_begin|> + 04:11 [视频-长播] <|video_begin|> + 04:11 [视频-长播] <|video_begin|> + 04:11 [视频-长播] <|video_begin|> + 04:48 [视频-长播] <|video_begin|> + 04:48 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 05:23 [广告-深度转化] <|ad_begin|> + 05:23 [广告-点击] <|ad_begin|> + 08:12 [广告-点击] <|ad_begin|> + 08:19 [视频-长播] <|video_begin|> + 10:01 [视频-长播] <|video_begin|> + 10:17 [广告-深度转化] <|ad_begin|> + 10:17 [广告-点击] <|ad_begin|> + 10:51 [广告-深度转化] <|ad_begin|> + 10:51 [广告-点击] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 12:18 [广告-点击] <|ad_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家，同时具备数据格式化能力，请根据以上用户交互历史，针对指定主题提取具有行为递进逻辑及内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心提取逻辑 (递进性准则)： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 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：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请围绕以下主题提取行为逻辑链： +主题：从被动娱乐到主动求知的认知转变 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动娱乐到主动求知的认知转变", + "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，我来分析一下这份用户行为数据。任务是需要我基于用户的交互时间线，梳理出一条从被动娱乐到主动求知的认知转变的行为逻辑链。 + +我首先注意到，这位用户的行为在很长一段时间内都表现出非常稳定的偏好。从2025年8月到11月，他的消费行为主要集中在两个明确的领域：一是户外活动，特别是钓鱼，他购买了大量的渔具，如诱饵笼 <|prod_begin|>、鱼钩 <|prod_begin|>、鱼竿 <|prod_begin|> 和各种鱼缸及配件 <|prod_begin|>，还关注了渔具清仓和钓竿推荐的直播间 <|living_begin|>；二是一些生活日用品和食品，比如男鞋 <|prod_begin|>、泡面 <|prod_begin|> 和手机 <|prod_begin|>。这期间，他的行为模式相对务实，围绕着个人爱好和生活必需品展开。 + +进入2025年12月后，我观察到一个显著的变化。用户的注意力开始大量被短剧广告所吸引。从12月8日开始，他密集地点击和深度转化了大量古装、仙侠、都市情感、悬疑等题材的短剧广告，例如 <|ad_begin|>、<|ad_begin|> 和 <|ad_begin|> 等。这种行为一直持续到2026年1月下旬，表明他在这段时间内，内容消费的主要动机是寻求剧情刺激和情感满足，这是一种典型的被动式娱乐消费。 + +然而，在2026年1月29日，我捕捉到了一个关键的转折点。在连续消费了近两个月的娱乐性短剧广告后，用户首次对一个完全不同类型的内容产生了兴趣。他点击了一则关于AI技术应用的广告 <|ad_begin|>。这个广告内容涵盖了AI在食品加工、农业、健身等多个领域的应用，强调的是学习和实践。我认为，这次点击行为标志着一个认知上的萌芽，用户的兴趣开始从纯粹的娱乐内容，向具有实用价值和知识性的技术领域延伸。 + +这个新萌发的兴趣点很快就得到了进一步的验证和深化。几天后的2026年2月5日，用户的行为表现出更强的主动性和目的性。他主动点击了一本名为《用 AI赚钱》的书籍商品 <|prod_begin|>。这表明，他对于AI的兴趣已经从泛化的技术认知，迅速聚焦到了一个非常具体且带有功利性的目标——“赚钱”。这不再是被动地接收信息，而是主动去寻找能将这项技术转化为生产力或商业价值的途径。 + +最关键的一步发生在同一天。在点击了这本书之后，用户迅速做出了购买决策，完成了对这本AI书籍 <|prod_begin|> 的购买。这个行为形成了从了解到决策的闭环，说明他已经完全认可了通过学习AI来提升自身价值的理念，并愿意为此进行知识付费。这与他之前大量消费免费短剧内容的行为模式形成了鲜明对比，标志着他从一个被动的娱乐内容消费者，向一个主动的知识探索者和付费学习者完成了转变。 + +综上所述，我认为用户的认知转变路径清晰可见。他最初沉浸在短剧等被动娱乐内容中，但在2026年1月29日，一次关于AI技术应用的广告点击 <|ad_begin|> 触发了他的兴趣点转移。这种兴趣很快就具体化为对“用AI赚钱”这一功利性目标的追求，体现在2月5日主动点击了相关书籍 <|prod_begin|>。最终，通过当天的购买行为 <|prod_begin|>，他完成了从被动娱乐到主动求知的关键一步。基于这条逻辑链，我推测该用户未来可能会继续关注与AI技术、在线课程或个人技能提升相关的内容，并有较大概率被能够带来实际收益的知识付费产品所吸引。 +{"logic_chain": {"name": "从被动娱乐到主动求知的认知转变", "events": [{"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣触发：在长期观看娱乐性短剧后，首次对一个明确指向“AI技术应用”的广告产生点击行为，标志着兴趣点从纯粹的剧情消费向技术应用领域偏移。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "需求聚焦：兴趣从泛化的“AI技术应用”收敛到具体的“用AI赚钱”，体现了从认知到功利性需求的转化，行为从被动接收广告转为主动点击带有明确商业目的的商品。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在同一天内完成了从点击到了解再到购买的完整决策流程，确认了将AI作为生产力工具的认知，并愿意为此付费，完成了从娱乐消费者到知识付费者的身份转变。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-02-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-22】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-26】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-03-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-04-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-05-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-06-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-09】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> +【2025-01-17】 + --:-- [直播-关注] <|living_begin|> +【2025-01-20】 + --:-- [直播-打赏] <|living_begin|> +【2025-02-06】 + --:-- [直播-关注] <|living_begin|> +【2025-02-07】 + --:-- [直播-关注] <|living_begin|> +【2025-03-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-10】 + --:-- [直播-关注] <|living_begin|> +【2025-07-13】 + --:-- [直播-关注] <|living_begin|> +【2025-07-14】 + --:-- [直播-关注] <|living_begin|> +【2025-07-15】 + --:-- [直播-关注] <|living_begin|> +【2025-07-16】 + --:-- [直播-关注] <|living_begin|> +【2025-08-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + 16:00 [广告-点击] <|ad_begin|> +【2025-12-11】 + 17:29 [广告-深度转化] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> +【2025-12-12】 + 15:14 [广告-点击] <|ad_begin|> +【2025-12-14】 + 02:36 [广告-深度转化] <|ad_begin|> + 02:36 [广告-点击] <|ad_begin|> +【2025-12-17】 + 14:09 [广告-点击] <|ad_begin|> +【2025-12-19】 + 16:52 [广告-点击] <|ad_begin|> +【2025-12-20】 + 02:15 [广告-深度转化] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 16:14 [广告-点击] <|ad_begin|> +【2025-12-21】 + 01:14 [广告-点击] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> +【2025-12-27】 + 03:16 [广告-点击] <|ad_begin|> +【2025-12-28】 + 01:58 [广告-点击] <|ad_begin|> + 02:05 [广告-点击] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 05:03 [广告-点击] <|ad_begin|> +【2026-01-02】 + 17:05 [广告-点击] <|ad_begin|> +【2026-01-04】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + 16:29 [广告-点击] <|ad_begin|> +【2026-01-09】 + 14:27 [广告-点击] <|ad_begin|> +【2026-01-10】 + 02:34 [广告-点击] <|ad_begin|> +【2026-01-12】 + 16:16 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-17】 + 02:04 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 13:51 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 14:54 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-23】 + 09:01 [视频-关注/点赞/长播] <|video_begin|> + 09:28 [视频-点赞/长播] <|video_begin|> + 16:31 [视频-转发/收藏/长播] <|video_begin|> + 16:41 [视频-转发/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-24】 + 04:07 [广告-点击] <|ad_begin|> + 04:12 [视频-评论/长播] <|video_begin|> + 05:03 [视频-点赞/长播] <|video_begin|> + 05:13 [广告-点击] <|ad_begin|> + 05:17 [视频-关注/长播] <|video_begin|> + 05:42 [视频-关注/长播] <|video_begin|> + 07:13 [广告-点击] <|ad_begin|> + 15:51 [视频-评论/长播] <|video_begin|> +【2026-01-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-26】 + 12:58 [视频-评论/长播] <|video_begin|> + 13:02 [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|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + 09:52 [视频-浏览] <|video_begin|> + 17:43 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + 03:47 [视频-评论/长播] <|video_begin|> + 04:02 [广告-点击] <|ad_begin|> + 04:21 [视频-评论/长播] <|video_begin|> + 04:22 [视频-评论/长播] <|video_begin|> + 07:43 [视频-点赞/长播] <|video_begin|> + 09:47 [视频-收藏/长播] <|video_begin|> + 10:37 [视频-评论/长播] <|video_begin|> + 10:45 [视频-转发/长播] <|video_begin|> + 12:19 [视频-转发/长播] <|video_begin|> + 12:28 [视频-浏览] <|video_begin|> + 13:56 [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|> +【2026-01-29】 + 02:41 [视频-点赞/长播] <|video_begin|> + 07:12 [视频-评论/长播] <|video_begin|> + 07:17 [视频-点赞/长播] <|video_begin|> + 07:44 [视频-点赞/长播] <|video_begin|> + 07:53 [视频-点赞/长播] <|video_begin|> + 07:53 [视频-评论/长播] <|video_begin|> + 08:26 [视频-点赞/长播] <|video_begin|> + 08:54 [视频-点赞/长播] <|video_begin|> + 08:54 [视频-评论/长播] <|video_begin|> + 09:14 [视频-点赞/长播] <|video_begin|> + 09:22 [视频-评论/长播] <|video_begin|> + 12:59 [视频-点赞/长播] <|video_begin|> + 14:11 [视频-评论/点赞/长播] <|video_begin|> + 14:21 [视频-评论/长播] <|video_begin|> + 16:06 [视频-评论/长播] <|video_begin|> + 16:42 [视频-浏览] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + 04:29 [广告-点击] <|ad_begin|> + 13:26 [视频-关注/长播] <|video_begin|> + 13:58 [视频-点赞/长播] <|video_begin|> + 18:00 [视频-转发/长播] <|video_begin|> + 18:09 [视频-转发/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + 05:47 [视频-转发/长播] <|video_begin|> + 06:58 [视频-转发/长播] <|video_begin|> + 06:58 [视频-转发/长播] <|video_begin|> + 09:50 [视频-评论/长播] <|video_begin|> + 11:35 [视频-评论/长播] <|video_begin|> + 13:58 [视频-点赞/长播] <|video_begin|> + 13:58 [视频-评论/长播] <|video_begin|> + 15:24 [视频-评论/长播] <|video_begin|> + 15:32 [视频-收藏/长播] <|video_begin|> + 15:44 [视频-点赞/长播] <|video_begin|> + 16:29 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:35 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 02:32 [视频-点赞/长播] <|video_begin|> + 03:17 [广告-点击] <|ad_begin|> + 04:52 [视频-评论/长播] <|video_begin|> + 05:14 [视频-点赞/长播] <|video_begin|> + 09:19 [视频-点赞/长播] <|video_begin|> + 09:19 [视频-转发/长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 14:22 [视频-收藏/长播] <|video_begin|> + 14:31 [视频-关注/点赞/长播] <|video_begin|> + 15:25 [视频-评论/长播] <|video_begin|> + 15:25 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-02】 + 05:18 [视频-转发/长播] <|video_begin|> + 05:34 [广告-点击] <|ad_begin|> + 10:46 [视频-转发/长播] <|video_begin|> + 12:29 [广告-点击] <|ad_begin|> + 17:28 [视频-评论/长播] <|video_begin|> + 17:28 [视频-评论/点赞/长播] <|video_begin|> + 18:34 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + 03:24 [广告-点击] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 05:46 [视频-转发/长播] <|video_begin|> + 07:54 [广告-点击] <|ad_begin|> + 08:57 [视频-关注/长播] <|video_begin|> + 12:49 [视频-收藏/长播] <|video_begin|> + 15:16 [视频-评论/长播] <|video_begin|> + 15:34 [视频-点赞/长播] <|video_begin|> + 17:08 [视频-收藏/长播] <|video_begin|> + 18:29 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + 09:41 [视频-点赞/长播] <|video_begin|> + 09:50 [视频-点赞/长播] <|video_begin|> +【2026-02-05】 + 05:42 [视频-关注/长播] <|video_begin|> + 05:55 [视频-长播] <|video_begin|> + 05:55 [视频-长播] <|video_begin|> + 06:00 [视频-长播] <|video_begin|> + 06:03 [视频-长播] <|video_begin|> + 06:03 [视频-长播] <|video_begin|> + 06:03 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:26 [视频-长播] <|video_begin|> + 06:26 [视频-长播] <|video_begin|> + 06:26 [视频-长播] <|video_begin|> + 06:26 [视频-长播] <|video_begin|> + 06:38 [视频-长播] <|video_begin|> + 06:38 [视频-长播] <|video_begin|> + 06:38 [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|> + 08:10 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:25 [视频-长播] <|video_begin|> + 08:25 [视频-长播] <|video_begin|> + 08:31 [视频-长播] <|video_begin|> + 08:31 [视频-长播] <|video_begin|> + 08:31 [视频-长播] <|video_begin|> + 08:31 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 09:17 [视频-长播] <|video_begin|> + 09:17 [视频-长播] <|video_begin|> + 09:19 [视频-长播] <|video_begin|> + 09:19 [视频-长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:41 [视频-长播] <|video_begin|> + 09:41 [视频-长播] <|video_begin|> + 09:41 [视频-长播] <|video_begin|> + 15:08 [视频-长播] <|video_begin|> + 15:08 [视频-长播] <|video_begin|> + 15:10 [视频-长播] <|video_begin|> + 15:10 [视频-长播] <|video_begin|> + 15:41 [视频-长播] <|video_begin|> + 15:41 [视频-长播] <|video_begin|> + 15:41 [视频-长播] <|video_begin|> + 15:41 [视频-评论/长播] <|video_begin|> + 15:51 [视频-长播] <|video_begin|> + 15:51 [视频-长播] <|video_begin|> + 15:51 [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）： 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 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<|prod_begin|>","logic":"认知扩展与对比：在已有矫正鞋垫购买经验的基础上，进一步点击了另一款产品，其标题增加了“成人”、“久站疼痛”等新变量，表明用户可能在对比不同产品的功能细节或将需求从儿童扩展至成人，是对前期解决方案的评估和优化。"}]}}<|im_end|> +<|im_start|>user +你是一个推荐系统助手，擅长根据用户属性与多域历史行为预测用户的内容偏好。 +用户多域历史行为： +用户在商品域: 浏览过的商品有 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>；购买/点击过的商品有 <|prod_begin|>；点击过的商品有 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户在视频域: 完播过的视频有 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>；长播过的视频有 <|video_begin|>, <|video_begin|>, <|video_begin|>；浏览过的视频有 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>。 +用户在广告域: 点击过的广告有 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, 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"初始触发：被动接收并点击了特定游戏《超自然行动组》的角色及玩法广告，建立了初步认知。"}, {"date": "2026-01-22", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "兴趣确认与加深：从广告的单向灌输，转变为主动观看并点赞与该游戏相关的二次创作内容，关注点从“玩法展示”转向了“游戏日常/趣味性”，交互深度增加。"}, {"date": "2026-01-24", "action": "[视频-点赞/长播] <|video_begin|>", "logic": "认知跃迁与横向拓展：兴趣从游戏日常的表层内容，跃迁至对游戏角色设计的深度解析（如设计灵感、演变过程），体现了对游戏世界观和美学构建的探究。同时，兴趣从《超自然行动组》拓展到同品类但有不同设计风格的《第五人格》，表明用户已形成对“角色设计”这一抽象概念的关注。"}, {"date": "2026-01-29", "action": "[商品-点击] <|prod_begin|>", "logic": "行为闭环：兴趣从线上的内容消费，延伸至对线下实体周边的关注，形成了从“了解到探索再到拥有”的完整兴趣闭环，是认知深度和粉丝身份认同的最终体现。"}]}}<|im_end|> + +好的，我来分析一下这份用户行为数据。任务是需要我基于用户的交互时间线，梳理出一条从被动娱乐到主动求知的认知转变的行为逻辑链。 + +我首先注意到，这位用户的行为在很长一段时间内都表现出非常稳定的偏好。从2025年8月到11月，他的消费行为主要集中在两个明确的领域：一是户外活动，特别是钓鱼，他购买了大量的渔具，如诱饵笼 <|prod_begin|>、鱼钩 <|prod_begin|>、鱼竿 <|prod_begin|> 和各种鱼缸及配件 <|prod_begin|>，还关注了渔具清仓和钓竿推荐的直播间 <|living_begin|>；二是一些生活日用品和食品，比如男鞋 <|prod_begin|>、泡面 <|prod_begin|> 和手机 <|prod_begin|>。这期间，他的行为模式相对务实，围绕着个人爱好和生活必需品展开。 + +进入2025年12月后，我观察到一个显著的变化。用户的注意力开始大量被短剧广告所吸引。从12月8日开始，他密集地点击和深度转化了大量古装、仙侠、都市情感、悬疑等题材的短剧广告，例如 <|ad_begin|>、<|ad_begin|> 和 <|ad_begin|> 等。这种行为一直持续到2026年1月下旬，表明他在这段时间内，内容消费的主要动机是寻求剧情刺激和情感满足，这是一种典型的被动式娱乐消费。 + +然而，在2026年1月29日，我捕捉到了一个关键的转折点。在连续消费了近两个月的娱乐性短剧广告后，用户首次对一个完全不同类型的内容产生了兴趣。他点击了一则关于AI技术应用的广告 <|ad_begin|>。这个广告内容涵盖了AI在食品加工、农业、健身等多个领域的应用，强调的是学习和实践。我认为，这次点击行为标志着一个认知上的萌芽，用户的兴趣开始从纯粹的娱乐内容，向具有实用价值和知识性的技术领域延伸。 + +这个新萌发的兴趣点很快就得到了进一步的验证和深化。几天后的2026年2月5日，用户的行为表现出更强的主动性和目的性。他主动点击了一本名为《用 AI赚钱》的书籍商品 <|prod_begin|>。这表明，他对于AI的兴趣已经从泛化的技术认知，迅速聚焦到了一个非常具体且带有功利性的目标——“赚钱”。这不再是被动地接收信息，而是主动去寻找能将这项技术转化为生产力或商业价值的途径。 + +最关键的一步发生在同一天。在点击了这本书之后，用户迅速做出了购买决策，完成了对这本AI书籍 <|prod_begin|> 的购买。这个行为形成了从了解到决策的闭环，说明他已经完全认可了通过学习AI来提升自身价值的理念，并愿意为此进行知识付费。这与他之前大量消费免费短剧内容的行为模式形成了鲜明对比，标志着他从一个被动的娱乐内容消费者，向一个主动的知识探索者和付费学习者完成了转变。 + +综上所述，我认为用户的认知转变路径清晰可见。他最初沉浸在短剧等被动娱乐内容中，但在2026年1月29日，一次关于AI技术应用的广告点击 <|ad_begin|> 触发了他的兴趣点转移。这种兴趣很快就具体化为对“用AI赚钱”这一功利性目标的追求，体现在2月5日主动点击了相关书籍 <|prod_begin|>。最终，通过当天的购买行为 <|prod_begin|>，他完成了从被动娱乐到主动求知的关键一步。基于这条逻辑链，我推测该用户未来可能会继续关注与AI技术、在线课程或个人技能提升相关的内容，并有较大概率被能够带来实际收益的知识付费产品所吸引。 +{"logic_chain": {"name": "从被动娱乐到主动求知的认知转变", "events": [{"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣触发：在长期观看娱乐性短剧后，首次对一个明确指向“AI技术应用”的广告产生点击行为，标志着兴趣点从纯粹的剧情消费向技术应用领域偏移。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "需求聚焦：兴趣从泛化的“AI技术应用”收敛到具体的“用AI赚钱”，体现了从认知到功利性需求的转化，行为从被动接收广告转为主动点击带有明确商业目的的商品。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在同一天内完成了从点击到了解再到购买的完整决策流程，确认了将AI作为生产力工具的认知，并愿意为此付费，完成了从娱乐消费者到知识付费者的身份转变。"}]}}<|im_end|> +{"logic_chain":{"name":"从泛化身体不适到特定功能矫正的健康需求探索","events":[{"date":"2025-04-21","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：购买针对婴幼儿特定行为（咬指甲）的辅助产品，表明用户关注点中存在儿童健康或行为矫正的需求场景。"},{"date":"2025-04-19","action":"[商品-购买] <|prod_begin|>","logic":"需求聚焦：从宽泛的儿童行为问题，收敛到对“扁平足”、“xo型腿”这类更具体的生理结构问题的解决方案探索，体现了对健康问题认知的深化和精准化。"},{"date":"2026-01-22","action":"[商品-点击] <|prod_begin|>","logic":"认知扩展与对比：在已有矫正鞋垫购买经验的基础上，进一步点击了另一款产品，其标题增加了“成人”、“久站疼痛”等新变量，表明用户可能在对比不同产品的功能细节或将需求从儿童扩展至成人，是对前期解决方案的评估和优化。"}]}}<|im_end|> +<|ad_begin|><|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 02:03:00,219 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 02:03:00,219 >> 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|2026-07-15 02:03:00] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 02:03:00] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 02:03:00,544 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-15 02:03:00,544 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 02:03:00,686 >> 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] 2026-07-15 02:03:00,984 >> 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] 2026-07-15 02:03:00,984 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-15 02:03:00] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 02:03:00] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 02:03:00] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 02:03:00] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 02:03:00] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 02:03:01,111 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 02:03:01,111 >> Num examples = 1,786 +[INFO|trainer.py:1471] 2026-07-15 02:03:01,111 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-15 02:03:01,111 >> Num update steps per epoch = 447 +[INFO|trainer.py:1473] 2026-07-15 02:03:01,111 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 02:03:01,111 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 02:03:01,111 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 02:03:01,111 >> Total optimization steps = 447 +[INFO|trainer.py:1479] 2026-07-15 02:03:01,111 >> Number of trainable parameters = 801,433,600 + 0%| | 0/447 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 0%| | 1/447 [00:08<1:02:08, 8.36s/it] 0%| | 2/447 [00:15<57:40, 7.78s/it] 1%| | 3/447 [00:23<57:11, 7.73s/it] 1%| | 4/447 [00:31<57:03, 7.73s/it] 1%| | 5/447 [00:37<53:57, 7.32s/it] 1%|▏ | 6/447 [00:45<53:59, 7.35s/it] 2%|▏ | 7/447 [00:51<52:15, 7.13s/it] 2%|▏ | 8/447 [00:59<52:27, 7.17s/it] 2%|▏ | 9/447 [01:06<53:05, 7.27s/it] 2%|▏ | 10/447 [01:14<55:32, 7.63s/it] {'loss': '1.977', 'grad_norm': '6.219', 'learning_rate': '3.857e-07', 'epoch': '0.0224'} + 2%|▏ | 10/447 [01:15<55:32, 7.63s/it] 2%|▏ | 11/447 [01:22<55:10, 7.59s/it] 3%|▎ | 12/447 [01:30<56:23, 7.78s/it] 3%|▎ | 13/447 [01:38<55:16, 7.64s/it] 3%|▎ | 14/447 [01:44<52:24, 7.26s/it] 3%|▎ | 15/447 [01:52<54:56, 7.63s/it] 4%|▎ | 16/447 [02:00<55:28, 7.72s/it] 4%|▍ | 17/447 [02:08<55:40, 7.77s/it] 4%|▍ | 18/447 [02:17<56:55, 7.96s/it] 4%|▍ | 19/447 [02:25<57:19, 8.04s/it] 4%|▍ | 20/447 [02:32<55:57, 7.86s/it] {'loss': '1.914', 'grad_norm': '7.594', 'learning_rate': '5.998e-07', 'epoch': '0.04479'} + 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Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '3538', 'train_samples_per_second': '0.505', 'train_steps_per_second': '0.126', 'train_loss': '1.928', 'epoch': '1'} + 100%|██████████| 447/447 [58:58<00:00, 6.50s/it] 100%|██████████| 447/447 [58:58<00:00, 7.92s/it] +[INFO|trainer.py:3815] 2026-07-15 03:01:59,220 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10 +[INFO|configuration_utils.py:533] 2026-07-15 03:01:59,221 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/config.json +[INFO|configuration_utils.py:816] 2026-07-15 03:01:59,221 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 03:02:00,169 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 03:02:00,191 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 198082373GF + train_loss = 1.9282 + train_runtime = 0:58:58.10 + train_samples_per_second = 0.505 + train_steps_per_second = 0.126 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v37_v29_r0_perception_lr6e7_ep10/training_loss.png +[WARNING|2026-07-15 03:02:00] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 03:02:00] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 03:02:00,284 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 'type': 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