From ef514207813bb048a040a5563d762bd6962017dc Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 16 Aug 2026 09:29:17 +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/v03_all_lr1e5_ep2 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + generation_config.json | 12 + model.safetensors | 3 + ...70850.ubuntu-System-Product-Name.1468345.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2331 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 160 ++ trainer_state.json | 1156 ++++++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 45899 bytes 16 files changed, 3949 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul06_01-00-50_ubuntu-System-Product-Name/events.out.tfevents.1783270850.ubuntu-System-Product-Name.1468345.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz 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diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..373a2c1 --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: OpenOneRec/OneReason-0.8B-pretrain-competition +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v03_all_lr1e5_ep2 + results: [] +--- + + + +# v03_all_lr1e5_ep2 + +This model is a fine-tuned version of [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the overnight_all 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: 1e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607063 +- 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: 2 + +### 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..9c6c2d3 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 2.0, + "total_flos": 3.833069438698537e+17, + "train_loss": 1.6445721892555754, + "train_runtime": 6416.9422, + "train_samples_per_second": 0.496, + "train_steps_per_second": 0.124 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..a7f1625 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ada9cf8995f4b12758f1f0d22decbaef429fc7d241ca54d1ab32bad642addce7 +size 1602902832 diff --git a/runs/Jul06_01-00-50_ubuntu-System-Product-Name/events.out.tfevents.1783270850.ubuntu-System-Product-Name.1468345.0 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false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": false, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..95fac58 --- /dev/null +++ b/train.log @@ -0,0 +1,2331 @@ +/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|2026-07-06 01:00:18] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:771] 2026-07-06 01:00:18,931 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 01:00:18,936 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-06 01:00:22,915 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 01:00:22,916 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-06 01:00:23,317 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 01:00:23,317 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-06 01:00:25] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/20260706_overnight/data/all.jsonl... + Converting format of dataset (num_proc=16): 100%|██████████| 32480/32480 [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" + } + ] + } +}/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。从提升普遍免疫力的黄芪多糖，到专门针对“犊牛”并以“快长”为目标的特定功能产品，这无疑是一次需求的深化。用户的关注点已经从宽泛的“防病”细化到了更具经济目的性的“促生长”。 + +随着用户在产品购买上的深入，我推断他可能会寻求更专业的知识来指导自己的实践。果然，在2025年11月30日，我发现他关注了一个兽药推荐达人的直播间 <|living_begin|>。这个直播间的主播标签非常专业，明确提到了“养殖问题解答”、“林可霉素讲解专家”和“氟尼辛讲解专家”。林可霉素和氟尼辛都是专业的兽用抗生素，这表明用户的认知需求已经从简单地购买成品，升级到了解具体的药物成分和使用方法。他不再满足于被动地看到什么就买什么，而是开始主动关注专业人士，希望获取系统性的知识。这个行为标志着他从一个单纯的产品消费者，向一个寻求专业指导的学习者转变，形成了一个从被动接收信息，到主动购买产品，再到系统性学习专业知识的完整闭环。 + +综合以上分析，我认为用户的行为清晰地展现了其在动物健康领域的焦虑是如何演化的。这个过程始于一次偶然的广告信息触达，随后通过购买通用性免疫产品进行初步尝试，接着需求深化至购买特定功能的生长促进剂，最终为了解决更深层次的问题，开始主动关注专业主播以获取系统性的兽药知识。整个链条逻辑清晰，层层递进。 + +因此，我倾向于认为，用户的健康焦虑演化路径如下： +首先，在2026年1月30日，用户通过点击一则关于处理小猫软便和饮水问题的广告 <|ad_begin|>，被动地接收了动物健康问题的相关信息。随后，在2025年11月17日，他主动购买了用于提升禽畜免疫力的黄芪多糖 <|prod_begin|>，开始探索解决方案。紧接着，在2025年11月19日，他的需求进一步聚焦，购买了针对性更强的“犊牛快长”产品 <|prod_begin|>。最后，在2025年11月30日，为了深化认知，他关注了一位专门讲解林可霉素、氟尼辛等专业兽药的达人主播 <|living_begin|>，寻求更专业的知识指导。 +{"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】： +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-06】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-01】 + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [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|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [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|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_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|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [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/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-14】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-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|> +【2026-01-17】 + --:-- [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/打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-18】 + --:-- [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/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + --:-- [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/首次打赏] <|living_begin|> + 19:08 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:53 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 03:37 [视频-点赞/长播] <|video_begin|> + 12:02 [广告-深度转化] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 12:45 [广告-深度转化] <|ad_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 18:51 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:27 [广告-深度转化] <|ad_begin|> + 23:42 [视频-长播] <|video_begin|> +【2026-01-23】 + 11:38 [视频-长播] <|video_begin|> + 12:14 [广告-深度转化] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 13:05 [广告-深度转化] <|ad_begin|> + 13:30 [广告-深度转化] <|ad_begin|> + 14:33 [广告-深度转化] <|ad_begin|> + 18:01 [广告-深度转化] <|ad_begin|> + 21:04 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 08:51 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 12:00 [广告-深度转化] <|ad_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:41 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 16:33 [广告-深度转化] <|ad_begin|> + 17:14 [广告-深度转化] <|ad_begin|> + 18:02 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:20 [广告-深度转化] <|ad_begin|> +【2026-01-25】 + 02:13 [广告-深度转化] <|ad_begin|> + 02:14 [视频-长播] <|video_begin|> + 02:35 [广告-深度转化] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 05:08 [广告-深度转化] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 12:19 [广告-深度转化] <|ad_begin|> + 12:40 [广告-深度转化] <|ad_begin|> + 13:07 [广告-深度转化] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 15:24 [广告-深度转化] <|ad_begin|> + 18:04 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + 06:44 [广告-深度转化] <|ad_begin|> + 07:09 [广告-深度转化] <|ad_begin|> + 09:28 [广告-深度转化] <|ad_begin|> + 10:58 [广告-深度转化] <|ad_begin|> + 21:05 [广告-深度转化] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 06:38 [广告-深度转化] <|ad_begin|> + 08:41 [广告-深度转化] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 12:37 [广告-深度转化] <|ad_begin|> + 12:43 [广告-深度转化] <|ad_begin|> + 13:28 [广告-深度转化] <|ad_begin|> + 16:29 [广告-深度转化] <|ad_begin|> + 16:48 [广告-深度转化] <|ad_begin|> + 16:53 [广告-深度转化] <|ad_begin|> +【2026-01-28】 + 03:39 [广告-深度转化] <|ad_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:31 [视频-长播] <|video_begin|> + 04:31 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:56 [视频-长播] <|video_begin|> + 11:58 [广告-深度转化] <|ad_begin|> + 12:09 [广告-深度转化] <|ad_begin|> + 13:02 [广告-深度转化] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 15:27 [广告-深度转化] <|ad_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 17:44 [广告-深度转化] <|ad_begin|> + 18:50 [广告-深度转化] <|ad_begin|> + 19:24 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:01 [广告-深度转化] <|ad_begin|> + 20:18 [广告-深度转化] <|ad_begin|> + 22:15 [广告-深度转化] <|ad_begin|> + 22:57 [视频-长播] <|video_begin|> + 22:57 [视频-关注/长播] <|video_begin|> +【2026-01-29】 + 01:01 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 12:28 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:12 [广告-点击] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:35 [广告-深度转化] <|ad_begin|> + 13:35 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:15 [广告-点击] <|ad_begin|> + 17:29 [广告-深度转化] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> + 17:43 [广告-点击] <|ad_begin|> + 17:56 [广告-点击] <|ad_begin|> + 18:01 [广告-点击] <|ad_begin|> + 18:25 [广告-点击] <|ad_begin|> + 18:25 [广告-点击] <|ad_begin|> + 18:32 [广告-点击] <|ad_begin|> + 18:37 [视频-长播] <|video_begin|> + 18:39 [视频-长播] <|video_begin|> + 18:39 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + 03:33 [广告-点击] <|ad_begin|> + 10:36 [广告-深度转化] <|ad_begin|> + 10:36 [广告-点击] <|ad_begin|> + 13:25 [广告-点击] <|ad_begin|> + 15:06 [广告-点击] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> + 15:19 [广告-点击] <|ad_begin|> + 15:22 [广告-点击] <|ad_begin|> + 15:23 [广告-点击] <|ad_begin|> + 15:42 [广告-深度转化] <|ad_begin|> + 15:42 [广告-点击] <|ad_begin|> + 16:12 [视频-转发/长播] <|video_begin|> + 16:45 [广告-深度转化] <|ad_begin|> + 16:45 [广告-点击] <|ad_begin|> + 16:49 [视频-长播] <|video_begin|> + 17:06 [广告-点击] <|ad_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:14 [视频-长播] <|video_begin|> + 17:56 [广告-点击] <|ad_begin|> + 17:58 [广告-点击] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> + 19:19 [视频-转发/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 19:41 [视频-长播] <|video_begin|> +【2026-01-31】 + 04:46 [广告-点击] <|ad_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|> + 13:55 [广告-点击] <|ad_begin|> + 14:03 [广告-深度转化] <|ad_begin|> + 14:03 [广告-点击] <|ad_begin|> + 14:30 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + 04:07 [广告-点击] <|ad_begin|> + 05:54 [视频-长播] <|video_begin|> + 05:54 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 07:33 [广告-点击] <|ad_begin|> + 07:59 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 09:59 [广告-点击] <|ad_begin|> + 11:44 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + 02:01 [广告-点击] <|ad_begin|> + 02:04 [广告-深度转化] <|ad_begin|> + 02:04 [广告-点击] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:09 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 02:19 [广告-点击] <|ad_begin|> + 02:20 [广告-点击] <|ad_begin|> + 02:23 [广告-点击] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:50 [广告-点击] <|ad_begin|> + 07:46 [视频-长播] <|video_begin|> + 07:46 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 09:22 [广告-点击] <|ad_begin|> + 15:13 [广告-点击] <|ad_begin|> + 15:38 [广告-点击] <|ad_begin|> + 15:40 [广告-点击] <|ad_begin|> + 15:44 [广告-点击] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 16:14 [广告-点击] <|ad_begin|> + 16:21 [广告-点击] <|ad_begin|> + 16:22 [广告-点击] <|ad_begin|> + 16:41 [广告-点击] <|ad_begin|> + 23:25 [广告-点击] <|ad_begin|> +【2026-02-03】 + 15:26 [广告-点击] <|ad_begin|> +【2026-02-04】 + 09:15 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 15:27 [广告-点击] <|ad_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）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +1.场景驱动协同：交互 B 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：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请面向以下主题提取行为逻辑链： +主题：从泛化祈福到特定化、功能化的玄学消费演进 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化祈福到特定化、功能化的玄学消费演进", + "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，我来分析一下这项任务。首先，任务要求我基于用户的交互历史，挖掘出一条关于“从泛化祈福到特定化、功能化的玄学消费演进”的行为逻辑链。这意味着我需要从用户的购买和点击行为中，找到一条线索，展示其对玄学或祈福类商品的兴趣是如何从一个模糊、宽泛的概念，逐渐变得具体、有针对性的。 + +我仔细梳理了用户的行为时间线，发现他在消费方面展现出多元化的偏好。早期，他的购物主要集中在性价比高的日用品和食品上，比如购买特价男鞋 <|prod_begin|>、速食粉丝 <|prod_begin|> 和猪肉小笼包 <|prod_begin|>，这反映了他对实用和便利性的基本需求。然而，从2025年10月开始，我注意到一个非常有趣的转变。 + +在2025年10月9日，用户首次购买了一件带有明显祈福寓意的商品——一个黄铜葫芦天官赐福钥匙扣 <|prod_begin|>。“天官赐福”是一个非常普适和泛化的祝福概念，几乎适用于所有场景，这在我看来是用户玄学兴趣的初步萌芽，一种比较随意的、不带特定目的的尝试。 + +紧接着，大约一个月后，在11月10日，我看到他再次购买了同类型的“天官赐福”福牌钥匙扣 <|prod_begin|>。但这次的商品有一个关键的不同点：它明确标注了“2026丙午马年”，并且带有“老师推荐”的标签。这对我来说是一个重要的信号。用户的需求不再是简单的“买个福牌”，而是开始关注商品的“时效性”（马年特定款），并且开始信任或受到某个圈子内“老师”的影响。这表明他的兴趣正在从泛化向有指导的、更具针对性的方向发展。 + +随后，在12月15日，用户的兴趣点发生了一次显著的跨越。他购买了一个“布宫礼赞藏粉古法吊坠” <|prod_begin|>。从商品名称中的“布宫”和“藏粉”这些关键词，我推断他开始探索不同文化体系下的玄学产品，从之前主流的道家或民间信仰体系，转向了带有藏传佛教色彩的物品。这代表着他的认知在横向扩展，不再满足于单一的文化符号，而是在进行更深度的探索和比较。 + +最后，在12月18日，我观察到用户点击了一款“五路貔貅手串” <|prod_begin|>。这是一个非常关键的节点。与“天官赐福”这种宽泛的祝福不同，“貔貅”在传统文化中具有非常明确的功能指向——招财。这意味着用户的需求已经从模糊的“祈求好运、保平安”，收敛到了一个非常具体且功能化的目标上，即“求财”。这个行为标志着他的玄学消费动机已经完成了从发散探索到聚焦需求的演变。 + +综合来看，用户的行为清晰地展示了一个从浅到深的兴趣演进过程。他最初只是出于泛化的祈福心态购买了天官赐福钥匙扣 <|prod_begin|>，随后在“老师推荐”的影响下开始关注具有特定年份的同类商品 <|prod_begin|>，显示出兴趣的强化。接着，他通过购买布宫礼赞吊坠 <|prod_begin|>，将探索范围扩大到不同的文化体系。最终，他的兴趣收敛，通过点击五路貔貅手串 <|prod_begin|>，明确指向了“招财”这一具体功能。因此，我倾向于认为，未来向他推荐具有更细分、更具体功能指向的玄学或文化商品，有较大概率被他所吸引。 +{"logic_chain": {"name": "从泛化祈福到特定化、功能化的玄学消费演进", "events": [{"date": "2025-10-09", "action": "[商品-购买] <|prod_begin|>", "logic": "初始触发：购买泛化的、带有普适性祝福寓意（天官赐福）的玄学商品，作为兴趣起点。"}, {"date": "2025-11-10", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣强化与时效性认知：从通用款转向具有明确年份（2026马年）指向的同类商品，并带有“老师推荐”标签，表明用户开始接受特定圈层的引导，认知从“随意买”向“跟着买”演进。"}, {"date": "2025-12-15", "action": "[商品-购买] <|prod_begin|>", "logic": "认知分化与深度探索：兴趣从汉地主流的“天官赐福”体系，首次转向具有明确地域和文化符号（布宫、藏粉）的藏传佛教体系，实现了文化圈层的跨越，是显著的认知增量。"}, {"date": "2025-12-18", "action": "[商品-点击] <|prod_begin|>", "logic": "需求聚焦与功能收敛：在经历了泛化祈福和文化体系探索后，用户的兴趣收敛到具有明确“招财”功能的“五路貔貅”上。这标志着用户的需求从“保平安”的模糊概念，聚焦到了“求财”这一具体目标，完成了从发散到聚焦的认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-04】 + --:-- [直播-关注] <|living_begin|> +【2025-02-01】 + --:-- [直播-关注] <|living_begin|> +【2025-02-04】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [直播-关注] <|living_begin|> +【2025-07-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + 09:45 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + 05:04 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + 11:56 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + 09:02 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 14:21 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 11:50 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 05:03 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 06:28 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 02:52 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:29 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 02:02 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + 08:54 [广告-深度转化] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 03:14 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:50 [广告-深度转化] <|ad_begin|> + 13:50 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 02:52 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 03:52 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 19:15 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 06:05 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 06:03 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 05:29 [广告-深度转化] <|ad_begin|> + 08:27 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:44 [广告-深度转化] <|ad_begin|> + 13:07 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 13:52 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 09:35 [广告-深度转化] <|ad_begin|> + 09:35 [广告-点击] <|ad_begin|> + 10:01 [广告-深度转化] <|ad_begin|> + 10:01 [广告-点击] <|ad_begin|> + 22:07 [广告-深度转化] <|ad_begin|> + 22:07 [广告-点击] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> +【2026-01-16】 + 05:43 [广告-深度转化] <|ad_begin|> + 05:43 [广告-点击] <|ad_begin|> + 08:05 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> +【2026-01-17】 + 02:29 [广告-深度转化] <|ad_begin|> + 02:29 [广告-点击] <|ad_begin|> + 06:08 [广告-深度转化] <|ad_begin|> + 06:08 [广告-点击] <|ad_begin|> + 15:37 [广告-深度转化] <|ad_begin|> + 15:37 [广告-点击] <|ad_begin|> +【2026-01-19】 + 01:11 [广告-点击] <|ad_begin|> + 07:35 [广告-点击] <|ad_begin|> + 08:30 [广告-点击] <|ad_begin|> +【2026-01-20】 + 06:17 [广告-深度转化] <|ad_begin|> + 06:17 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 05:42 [视频-点赞/长播] <|video_begin|> + 07:03 [广告-点击] <|ad_begin|> + 09:04 [视频-关注/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|> + 20:38 [视频-关注/长播] <|video_begin|> + 21:47 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:12 [广告-深度转化] <|ad_begin|> + 03:12 [广告-点击] <|ad_begin|> + 05:39 [广告-深度转化] <|ad_begin|> + 05:39 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:20 [广告-深度转化] <|ad_begin|> + 11:20 [广告-点击] <|ad_begin|> + 13:55 [广告-深度转化] <|ad_begin|> + 13:55 [广告-点击] <|ad_begin|> + 21:09 [视频-关注/点赞/长播] <|video_begin|> + 21:34 [视频-关注/长播] <|video_begin|> + 21:44 [视频-关注/点赞/长播] <|video_begin|> + 23:32 [视频-关注/点赞/长播] <|video_begin|> +【2026-01-24】 + 02:25 [广告-深度转化] <|ad_begin|> + 02:25 [广告-点击] <|ad_begin|> + 05:31 [广告-深度转化] <|ad_begin|> + 05:31 [广告-点击] <|ad_begin|> + 06:27 [视频-点赞/长播] <|video_begin|> + 06:27 [视频-关注/点赞/长播] <|video_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:56 [广告-点击] <|ad_begin|> + 12:09 [广告-点击] <|ad_begin|> + 12:18 [广告-点击] <|ad_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 13:16 [广告-点击] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> +【2026-01-25】 + 01:08 [广告-深度转化] <|ad_begin|> + 01:08 [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|> + 10:47 [广告-点击] <|ad_begin|> + 14:09 [广告-点击] <|ad_begin|> +【2026-01-26】 + 03:28 [广告-深度转化] <|ad_begin|> + 03:28 [广告-点击] <|ad_begin|> + 05:52 [广告-点击] <|ad_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:51 [广告-深度转化] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> +【2026-01-27】 + 10:21 [视频-关注/长播] <|video_begin|> + 12:06 [广告-点击] <|ad_begin|> + 12:27 [视频-关注/点赞/长播] <|video_begin|> + 12:40 [视频-关注/点赞/长播] <|video_begin|> + 13:01 [视频-关注/长播] <|video_begin|> + 13:05 [视频-关注/点赞/长播] <|video_begin|> + 13:05 [视频-关注/点赞/长播] <|video_begin|> +【2026-01-28】 + 00:52 [视频-点赞/长播] <|video_begin|> + 01:10 [视频-关注/点赞/长播] <|video_begin|> + 01:27 [视频-点赞/长播] <|video_begin|> + 03:21 [视频-关注/点赞/长播] <|video_begin|> + 04:10 [广告-深度转化] <|ad_begin|> + 04:10 [广告-点击] <|ad_begin|> + 07:28 [广告-深度转化] <|ad_begin|> + 07:28 [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-购买] <|prod_begin|> + 11:24 [视频-点赞/长播] <|video_begin|> + 12:19 [视频-点赞/长播] <|video_begin|> + 13:21 [视频-点赞/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|> + 10:42 [视频-点赞/长播] <|video_begin|> + 11:06 [广告-深度转化] <|ad_begin|> + 11:06 [广告-点击] <|ad_begin|> + 12:58 [广告-点击] <|ad_begin|> + 13:25 [视频-点赞/长播] <|video_begin|> + 21:36 [视频-关注/点赞/长播] <|video_begin|> +【2026-01-30】 + 00:19 [广告-深度转化] <|ad_begin|> + 00:19 [广告-点击] <|ad_begin|> + 01:40 [广告-深度转化] <|ad_begin|> + 01:40 [广告-点击] <|ad_begin|> + 03:16 [广告-点击] <|ad_begin|> + 06:21 [广告-深度转化] <|ad_begin|> + 06:21 [广告-点击] <|ad_begin|> + 07:41 [广告-深度转化] <|ad_begin|> + 07:41 [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-购买] <|prod_begin|> + 11:45 [视频-收藏/长播] <|video_begin|> +【2026-01-31】 + 00:23 [视频-关注/点赞/长播] <|video_begin|> + 00:51 [视频-关注/点赞/长播] <|video_begin|> + 01:38 [广告-深度转化] <|ad_begin|> + 01:38 [广告-点击] <|ad_begin|> + 06:01 [广告-深度转化] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:59 [广告-深度转化] <|ad_begin|> + 12:59 [广告-点击] <|ad_begin|> +【2026-02-01】 + 09:19 [广告-点击] <|ad_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 13:16 [广告-点击] <|ad_begin|> +【2026-02-02】 + 01:11 [视频-关注/点赞/长播] <|video_begin|> + 01:40 [视频-关注/点赞/长播] <|video_begin|> + 01:55 [广告-点击] <|ad_begin|> + 02:22 [广告-深度转化] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 03:50 [广告-深度转化] <|ad_begin|> + 03:50 [广告-点击] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 07:51 [广告-深度转化] <|ad_begin|> + 07:51 [广告-点击] <|ad_begin|> + 23:10 [广告-深度转化] <|ad_begin|> + 23:10 [广告-点击] <|ad_begin|> +【2026-02-03】 + 03:19 [视频-点赞/长播] <|video_begin|> + 03:44 [广告-深度转化] <|ad_begin|> + 03:44 [广告-点击] <|ad_begin|> + 06:30 [视频-长播] <|video_begin|> + 06:30 [视频-关注/收藏/点赞/长播] <|video_begin|> + 06:58 [视频-长播] <|video_begin|> + 06:58 [视频-长播] <|video_begin|> + 10:08 [广告-点击] <|ad_begin|> + 10:33 [视频-长播] <|video_begin|> + 10:33 [视频-点赞/长播] <|video_begin|> + 11:56 [广告-深度转化] <|ad_begin|> + 11:56 [广告-点击] <|ad_begin|> + 14:26 [视频-长播] <|video_begin|> + 14:32 [视频-长播] <|video_begin|> + 23:00 [视频-长播] <|video_begin|> +【2026-02-04】 + 07:38 [视频-长播] <|video_begin|> + 07:38 [视频-长播] <|video_begin|> + 07:38 [视频-长播] <|video_begin|> + 08:31 [视频-长播] <|video_begin|> + 09:51 [视频-长播] <|video_begin|> + 10:19 [视频-长播] <|video_begin|> + 10:19 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:30 [视频-长播] <|video_begin|> + 10:30 [视频-长播] <|video_begin|> + 10:30 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> + 11:00 [广告-深度转化] <|ad_begin|> + 11:00 [广告-点击] <|ad_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 13:00 [视频-关注/点赞/长播] <|video_begin|> + 13:53 [视频-长播] <|video_begin|> + 13:53 [视频-长播] <|video_begin|> + 22:33 [视频-长播] <|video_begin|> + 22:51 [视频-长播] <|video_begin|> + 23:03 [视频-长播] <|video_begin|> + 23:03 [视频-长播] <|video_begin|> + 23:22 [广告-深度转化] <|ad_begin|> + 23:22 [广告-点击] <|ad_begin|> + 23:52 [视频-长播] <|video_begin|> + 23:52 [视频-长播] <|video_begin|> + 23:55 [视频-长播] <|video_begin|> + 23:55 [视频-长播] <|video_begin|> + 23:55 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:09 [视频-关注/点赞/长播] <|video_begin|> + 00:14 [广告-点击] <|ad_begin|> + 00:20 [视频-长播] <|video_begin|> + 00:20 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:25 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:39 [视频-长播] <|video_begin|> + 00:39 [视频-长播] <|video_begin|> + 01:00 [视频-长播] <|video_begin|> + 01:25 [视频-浏览] <|video_begin|> + 01:28 [视频-长播] <|video_begin|> + 02:16 [广告-点击] <|ad_begin|> + 02:50 [视频-长播] <|video_begin|> + 02:54 [视频-长播] <|video_begin|> + 03:28 [视频-长播] <|video_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 05:26 [视频-长播] <|video_begin|> + 06:08 [视频-长播] <|video_begin|> + 06:08 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 09:05 [广告-深度转化] <|ad_begin|> + 09:05 [广告-点击] <|ad_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:50 [广告-点击] <|ad_begin|> + 14:04 [广告-点击] <|ad_begin|> + 14:12 [广告-深度转化] <|ad_begin|> + 14:12 [广告-点击] <|ad_begin|> + 15:23 [广告-点击] <|ad_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）： 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。 +请根据以下主题提取行为逻辑链： +主题：从被动娱乐到主动寻求情感慰藉的路径探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动娱乐到主动寻求情感慰藉的路径探索", + "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" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我将以推荐分析师的视角，对这项任务进行推理。 + +我的任务是分析用户的行为时间线，梳理出一条从被动娱乐消费到主动寻求情感慰藉的路径。 + +首先，我注意到在2025年12月下旬到2026年1月初，用户表现出对短剧广告的强烈兴趣。这段时间里，他深度转化了大量广告，例如关于家庭关系与情感纠葛的《重回黄金九八:从逆袭到巅峰》<|ad_begin|>，以及涉及悬疑、复仇、逆袭等元素的《潜龙出山》<|ad_begin|>和《下山被退婚，趁热救活女总裁》<|ad_begin|>等。这些广告内容大多是虚构的、戏剧冲突强烈的爽文式剧情。我认为，在这个阶段，用户的内容消费动机主要是被动接收平台推送的娱乐内容，以消遣和寻求即时刺激为主。 + +然而，到了2026年1月22日，我观察到一个关键的行为转变。用户不再仅仅是被动地看广告，而是主动地对一则关于消防员陈亮为救人牺牲的视频 <|video_begin|> 进行了关注、点赞和长时间观看。这个视频的核心是“家庭责任”与“舍己为人”的奉献精神，是源于真实社会事件的纪实内容。这次主动且深度的互动，标志着用户的兴趣点开始从虚构的戏剧冲突，向更具现实意义和情感共鸣的内容迁移。这不再是简单的娱乐消遣，而是开始被真实世界中的深刻情感所触动。 + +这种兴趣迁移在几天后得到了进一步的印证。在1月29日，用户再次主动关注、点赞并长时观看了一则关于孝心男孩赵文龙为救母徒步求医的视频 <|video_begin|>。这个故事同样聚焦于“家庭责任”和“孝道”这类具有强烈情感冲击力和正向社会价值的主题。连续两次对同类型真实感人故事的深度互动，让我推断用户的需求已经发生了质变。他不再满足于短剧带来的浅层娱乐，而是开始主动寻找能够引发深度情感共鸣、传递正能量的内容，以获得情感上的慰藉和价值上的认同。 + +综合来看，我认为用户的行为路径清晰地展现了一个从被动娱乐到主动寻求情感慰藉的演变过程。最初，他被动地消费大量以“情感纠葛”和“逆袭”为卖点的短剧广告，满足基本的娱乐需求。随后，他开始主动关注并深度互动关于“家庭责任”与“牺牲奉献”的真实社会纪实内容，显示出其兴趣点已从戏剧化冲突转向了对真实情感的共鸣。这一系列行为表明，用户的内容消费动机已经从单纯的消遣，升级为寻求更深层次的情感慰藉和正向价值的确认。因此，我倾向于认为，用户未来可能会对传递亲情、奉献、孝道等主题的真实感人故事或纪实类内容有更高的偏好。 + +我梳理出的行为逻辑链如下： +用户首先深度转化了以家庭关系和情感纠葛为主题的短剧广告，如《重回黄金九八:从逆袭到巅峰》<|ad_begin|>，这表明其对该类娱乐内容有初步兴趣。随后，他的行为从被动接收转向主动互动，关注、点赞并长时观看了关于消防员为家庭责任而牺牲的真实纪实视频 <|video_begin|>，兴趣点从戏剧冲突深化为真实情感共鸣。最后，他再次主动关注并深度互动了另一则关于孝心男孩为母求医的感人故事 <|video_begin|>，这确认了他的兴趣已聚焦于“亲情奉献”这一更具正能量的主题，完成了从消遣娱乐到主动寻求情感慰藉的转变。 +{"logic_chain": {"name": "从被动娱乐到主动寻求情感慰藉的路径探索", "events": [{"date": "2025-12-22", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户被动接收并深度转化了大量以“家庭关系”、“情感纠葛”、“逆袭”为主题的短剧广告，表明其对该类内容存在初步兴趣。"}, {"date": "2026-01-22", "action": "[视频-关注/点赞/长播] <|video_begin|>", "logic": "兴趣深化与行为转变：用户从观看虚构的短剧，转向主动关注、点赞并长时观看真实社会事件中关于“家庭责任”与“牺牲”的纪实内容。行为从被动接收广告转为主动深度互动，兴趣点从“戏剧冲突”深化为“真实情感共鸣”。"}, {"date": "2026-01-29", "action": "[视频-关注/点赞/长播] <|video_begin|>", "logic": "主题聚焦与认知确认：用户再次主动关注并深度互动另一则关于“家庭责任”与“孝道”的真实感人故事。这确认了其兴趣已从宽泛的“情感纠葛”收敛至更具正能量和社会价值的“亲情奉献”主题，完成了从消遣娱乐到寻求情感慰藉和正向价值认同的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + 06:58 [广告-深度转化] <|ad_begin|> + 10:31 [广告-深度转化] <|ad_begin|> + 10:33 [广告-深度转化] <|ad_begin|> + 10:36 [广告-深度转化] <|ad_begin|> + 10:36 [广告-深度转化] <|ad_begin|> + 11:03 [广告-深度转化] <|ad_begin|> + 11:08 [广告-深度转化] <|ad_begin|> + 11:15 [广告-深度转化] <|ad_begin|> + 12:47 [广告-深度转化] <|ad_begin|> + 12:50 [广告-深度转化] <|ad_begin|> + 13:35 [广告-深度转化] <|ad_begin|> + 13:39 [广告-深度转化] <|ad_begin|> + 15:39 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 22:19 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 17:28 [广告-深度转化] <|ad_begin|> + 20:24 [广告-深度转化] <|ad_begin|> + 22:38 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 16:30 [广告-深度转化] <|ad_begin|> + 22:19 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 18:19 [广告-深度转化] <|ad_begin|> + 23:04 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 17:11 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [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|> + --:-- [商品-购买] <|prod_begin|> + 17:17 [广告-深度转化] <|ad_begin|> + 22:45 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 12:30 [广告-深度转化] <|ad_begin|> + 14:19 [广告-深度转化] <|ad_begin|> + 14:44 [广告-点击] <|ad_begin|> + 22:41 [广告-深度转化] <|ad_begin|> + 22:41 [广告-点击] <|ad_begin|> +【2026-01-10】 + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 12:29 [广告-点击] <|ad_begin|> + 12:30 [广告-深度转化] <|ad_begin|> + 12:30 [广告-点击] <|ad_begin|> + 12:47 [广告-深度转化] <|ad_begin|> + 12:47 [广告-点击] <|ad_begin|> + 22:03 [广告-深度转化] <|ad_begin|> + 22:03 [广告-点击] <|ad_begin|> +【2026-01-12】 + 05:38 [广告-深度转化] <|ad_begin|> + 05:38 [广告-点击] <|ad_begin|> + 07:02 [广告-深度转化] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:50 [广告-深度转化] <|ad_begin|> + 17:50 [广告-点击] <|ad_begin|> +【2026-01-13】 + 06:59 [广告-深度转化] <|ad_begin|> + 06:59 [广告-点击] <|ad_begin|> + 07:04 [广告-点击] <|ad_begin|> + 07:27 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:08 [广告-深度转化] <|ad_begin|> + 20:08 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [商品-购买] <|prod_begin|> + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> +【2026-01-15】 + 04:51 [广告-点击] <|ad_begin|> + 04:51 [广告-深度转化] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 06:21 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> + 13:33 [广告-深度转化] <|ad_begin|> + 13:33 [广告-点击] <|ad_begin|> + 13:34 [广告-深度转化] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> + 16:59 [广告-点击] <|ad_begin|> + 17:08 [广告-深度转化] <|ad_begin|> + 17:08 [广告-点击] <|ad_begin|> +【2026-01-16】 + 05:08 [广告-深度转化] <|ad_begin|> + 05:08 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:17 [广告-点击] <|ad_begin|> + 14:18 [广告-深度转化] <|ad_begin|> + 14:18 [广告-点击] <|ad_begin|> + 15:10 [广告-点击] <|ad_begin|> + 16:01 [广告-深度转化] <|ad_begin|> + 16:01 [广告-点击] <|ad_begin|> + 18:03 [广告-点击] <|ad_begin|> + 19:33 [广告-深度转化] <|ad_begin|> + 19:33 [广告-点击] <|ad_begin|> + 22:23 [广告-深度转化] <|ad_begin|> + 22:23 [广告-点击] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> + 22:36 [广告-点击] <|ad_begin|> + 22:51 [广告-深度转化] <|ad_begin|> + 22:51 [广告-点击] <|ad_begin|> +【2026-01-17】 + 22:53 [广告-深度转化] <|ad_begin|> + 22:53 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + 22:41 [广告-深度转化] <|ad_begin|> + 22:41 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:26 [广告-深度转化] <|ad_begin|> + 22:26 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [商品-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|> + 11:18 [广告-点击] <|ad_begin|> +【2026-01-22】 + 09:26 [广告-点击] <|ad_begin|> + 11:30 [广告-点击] <|ad_begin|> + 17:01 [广告-点击] <|ad_begin|> + 17:15 [广告-深度转化] <|ad_begin|> + 17:15 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 07:49 [广告-深度转化] <|ad_begin|> + 07:49 [广告-点击] <|ad_begin|> + 10:45 [视频-点赞/长播] <|video_begin|> + 11:52 [视频-关注/长播] <|video_begin|> +【2026-01-24】 + 04:37 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + 00:40 [广告-深度转化] <|ad_begin|> + 00:40 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:15 [视频-转发/长播] <|video_begin|> + 13:53 [广告-点击] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + 07:17 [广告-点击] <|ad_begin|> + 07:25 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + --:-- [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|> + 14:44 [广告-深度转化] <|ad_begin|> + 14:44 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + 08:56 [广告-点击] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> + 17:49 [广告-点击] <|ad_begin|> + 22:19 [广告-点击] <|ad_begin|> + 22:22 [广告-点击] <|ad_begin|> + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> +【2026-01-31】 + 04:58 [广告-点击] <|ad_begin|> + 07:15 [广告-深度转化] <|ad_begin|> + 07:15 [广告-点击] <|ad_begin|> + 16:11 [广告-深度转化] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 17:52 [广告-深度转化] <|ad_begin|> + 17:52 [广告-点击] <|ad_begin|> +【2026-02-01】 + 12:59 [视频-关注/长播] <|video_begin|> + 13:14 [广告-深度转化] <|ad_begin|> + 13:14 [广告-点击] <|ad_begin|> + 21:18 [广告-深度转化] <|ad_begin|> + 21:18 [广告-点击] <|ad_begin|> + 21:19 [广告-深度转化] <|ad_begin|> + 21:19 [广告-点击] <|ad_begin|> + 22:23 [广告-深度转化] <|ad_begin|> + 22:23 [广告-点击] <|ad_begin|> +【2026-02-02】 + 16:54 [视频-浏览] <|video_begin|> + 20:27 [广告-点击] <|ad_begin|> +【2026-02-03】 + 07:25 [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|> +【2026-02-04】 + 12:23 [广告-点击] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> + 13:01 [视频-长播] <|video_begin|> + 13:01 [视频-长播] <|video_begin|> + 21:24 [广告-深度转化] <|ad_begin|> + 21:24 [广告-点击] <|ad_begin|> + 21:52 [广告-深度转化] <|ad_begin|> + 21:52 [广告-点击] <|ad_begin|> + 23:33 [广告-深度转化] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-02-05】 + 03:36 [视频-长播] <|video_begin|> + 03:36 [视频-长播] <|video_begin|> + 03:36 [视频-长播] <|video_begin|> + 04:55 [视频-长播] <|video_begin|> + 04:55 [视频-长播] <|video_begin|> + 05:05 [视频-长播] <|video_begin|> + 05:05 [视频-长播] <|video_begin|> + 05:05 [视频-长播] <|video_begin|> + 05:11 [视频-长播] <|video_begin|> + 05:11 [视频-长播] <|video_begin|> + 07:14 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:47 [视频-长播] <|video_begin|> + 07:47 [视频-长播] <|video_begin|> + 07:47 [视频-长播] <|video_begin|> + 07:47 [视频-长播] <|video_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> + 09:00 [视频-长播] <|video_begin|> + 09:12 [视频-长播] <|video_begin|> + 09:18 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:22 [广告-点击] <|ad_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:24 [视频-长播] <|video_begin|> + 11:04 [视频-长播] <|video_begin|> + 11:04 [视频-长播] <|video_begin|> + 11:04 [视频-长播] <|video_begin|> + 11:04 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:20 [视频-长播] <|video_begin|> + 11:37 [视频-长播] <|video_begin|> + 11:37 [视频-长播] <|video_begin|> + 11:37 [视频-长播] <|video_begin|> + 11:40 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-点赞/长播] <|video_begin|> + 13:05 [广告-深度转化] <|ad_begin|> + 13:05 [广告-点击] <|ad_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）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心判定逻辑（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 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"需求深化（从主食到零食）：用户在解决了基础喂食问题后，开始关注更精细化的需求，如“磨牙”、“训练奖励”。这表明用户的认知已从“喂饱”深化到“互动与健康”，增加了新的养宠维度。"}, {"date": "2026-01-15", "action": "[商品-购买] <|prod_begin|>", "logic": "认知跃迁（从食物到营养补剂）：用户的关注点从可见的食物（主食、零食）跃迁至不可见的微量元素层面（电解质、维生素）。这标志着用户开始系统性地考虑宠物的全面健康和营养均衡，是养宠认知从经验层面到科学层面的关键一步。"}]}}<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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<|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。从提升普遍免疫力的黄芪多糖，到专门针对“犊牛”并以“快长”为目标的特定功能产品，这无疑是一次需求的深化。用户的关注点已经从宽泛的“防病”细化到了更具经济目的性的“促生长”。 + +随着用户在产品购买上的深入，我推断他可能会寻求更专业的知识来指导自己的实践。果然，在2025年11月30日，我发现他关注了一个兽药推荐达人的直播间 <|living_begin|>。这个直播间的主播标签非常专业，明确提到了“养殖问题解答”、“林可霉素讲解专家”和“氟尼辛讲解专家”。林可霉素和氟尼辛都是专业的兽用抗生素，这表明用户的认知需求已经从简单地购买成品，升级到了解具体的药物成分和使用方法。他不再满足于被动地看到什么就买什么，而是开始主动关注专业人士，希望获取系统性的知识。这个行为标志着他从一个单纯的产品消费者，向一个寻求专业指导的学习者转变，形成了一个从被动接收信息，到主动购买产品，再到系统性学习专业知识的完整闭环。 + +综合以上分析，我认为用户的行为清晰地展现了其在动物健康领域的焦虑是如何演化的。这个过程始于一次偶然的广告信息触达，随后通过购买通用性免疫产品进行初步尝试，接着需求深化至购买特定功能的生长促进剂，最终为了解决更深层次的问题，开始主动关注专业主播以获取系统性的兽药知识。整个链条逻辑清晰，层层递进。 + +因此，我倾向于认为，用户的健康焦虑演化路径如下： +首先，在2026年1月30日，用户通过点击一则关于处理小猫软便和饮水问题的广告 <|ad_begin|>，被动地接收了动物健康问题的相关信息。随后，在2025年11月17日，他主动购买了用于提升禽畜免疫力的黄芪多糖 <|prod_begin|>，开始探索解决方案。紧接着，在2025年11月19日，他的需求进一步聚焦，购买了针对性更强的“犊牛快长”产品 <|prod_begin|>。最后，在2025年11月30日，为了深化认知，他关注了一位专门讲解林可霉素、氟尼辛等专业兽药的达人主播 <|living_begin|>，寻求更专业的知识指导。 +{"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|> + +好的，我来分析一下这项任务。首先，任务要求我基于用户的交互历史，挖掘出一条关于“从泛化祈福到特定化、功能化的玄学消费演进”的行为逻辑链。这意味着我需要从用户的购买和点击行为中，找到一条线索，展示其对玄学或祈福类商品的兴趣是如何从一个模糊、宽泛的概念，逐渐变得具体、有针对性的。 + +我仔细梳理了用户的行为时间线，发现他在消费方面展现出多元化的偏好。早期，他的购物主要集中在性价比高的日用品和食品上，比如购买特价男鞋 <|prod_begin|>、速食粉丝 <|prod_begin|> 和猪肉小笼包 <|prod_begin|>，这反映了他对实用和便利性的基本需求。然而，从2025年10月开始，我注意到一个非常有趣的转变。 + +在2025年10月9日，用户首次购买了一件带有明显祈福寓意的商品——一个黄铜葫芦天官赐福钥匙扣 <|prod_begin|>。“天官赐福”是一个非常普适和泛化的祝福概念，几乎适用于所有场景，这在我看来是用户玄学兴趣的初步萌芽，一种比较随意的、不带特定目的的尝试。 + +紧接着，大约一个月后，在11月10日，我看到他再次购买了同类型的“天官赐福”福牌钥匙扣 <|prod_begin|>。但这次的商品有一个关键的不同点：它明确标注了“2026丙午马年”，并且带有“老师推荐”的标签。这对我来说是一个重要的信号。用户的需求不再是简单的“买个福牌”，而是开始关注商品的“时效性”（马年特定款），并且开始信任或受到某个圈子内“老师”的影响。这表明他的兴趣正在从泛化向有指导的、更具针对性的方向发展。 + +随后，在12月15日，用户的兴趣点发生了一次显著的跨越。他购买了一个“布宫礼赞藏粉古法吊坠” <|prod_begin|>。从商品名称中的“布宫”和“藏粉”这些关键词，我推断他开始探索不同文化体系下的玄学产品，从之前主流的道家或民间信仰体系，转向了带有藏传佛教色彩的物品。这代表着他的认知在横向扩展，不再满足于单一的文化符号，而是在进行更深度的探索和比较。 + +最后，在12月18日，我观察到用户点击了一款“五路貔貅手串” <|prod_begin|>。这是一个非常关键的节点。与“天官赐福”这种宽泛的祝福不同，“貔貅”在传统文化中具有非常明确的功能指向——招财。这意味着用户的需求已经从模糊的“祈求好运、保平安”，收敛到了一个非常具体且功能化的目标上，即“求财”。这个行为标志着他的玄学消费动机已经完成了从发散探索到聚焦需求的演变。 + +综合来看，用户的行为清晰地展示了一个从浅到深的兴趣演进过程。他最初只是出于泛化的祈福心态购买了天官赐福钥匙扣 <|prod_begin|>，随后在“老师推荐”的影响下开始关注具有特定年份的同类商品 <|prod_begin|>，显示出兴趣的强化。接着，他通过购买布宫礼赞吊坠 <|prod_begin|>，将探索范围扩大到不同的文化体系。最终，他的兴趣收敛，通过点击五路貔貅手串 <|prod_begin|>，明确指向了“招财”这一具体功能。因此，我倾向于认为，未来向他推荐具有更细分、更具体功能指向的玄学或文化商品，有较大概率被他所吸引。 +{"logic_chain": {"name": "从泛化祈福到特定化、功能化的玄学消费演进", "events": [{"date": "2025-10-09", "action": "[商品-购买] <|prod_begin|>", "logic": "初始触发：购买泛化的、带有普适性祝福寓意（天官赐福）的玄学商品，作为兴趣起点。"}, {"date": "2025-11-10", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣强化与时效性认知：从通用款转向具有明确年份（2026马年）指向的同类商品，并带有“老师推荐”标签，表明用户开始接受特定圈层的引导，认知从“随意买”向“跟着买”演进。"}, {"date": "2025-12-15", "action": "[商品-购买] <|prod_begin|>", "logic": "认知分化与深度探索：兴趣从汉地主流的“天官赐福”体系，首次转向具有明确地域和文化符号（布宫、藏粉）的藏传佛教体系，实现了文化圈层的跨越，是显著的认知增量。"}, {"date": "2025-12-18", "action": "[商品-点击] <|prod_begin|>", "logic": "需求聚焦与功能收敛：在经历了泛化祈福和文化体系探索后，用户的兴趣收敛到具有明确“招财”功能的“五路貔貅”上。这标志着用户的需求从“保平安”的模糊概念，聚焦到了“求财”这一具体目标，完成了从发散到聚焦的认知闭环。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这项任务进行推理。 + +我的任务是分析用户的行为时间线，梳理出一条从被动娱乐消费到主动寻求情感慰藉的路径。 + +首先，我注意到在2025年12月下旬到2026年1月初，用户表现出对短剧广告的强烈兴趣。这段时间里，他深度转化了大量广告，例如关于家庭关系与情感纠葛的《重回黄金九八:从逆袭到巅峰》<|ad_begin|>，以及涉及悬疑、复仇、逆袭等元素的《潜龙出山》<|ad_begin|>和《下山被退婚，趁热救活女总裁》<|ad_begin|>等。这些广告内容大多是虚构的、戏剧冲突强烈的爽文式剧情。我认为，在这个阶段，用户的内容消费动机主要是被动接收平台推送的娱乐内容，以消遣和寻求即时刺激为主。 + +然而，到了2026年1月22日，我观察到一个关键的行为转变。用户不再仅仅是被动地看广告，而是主动地对一则关于消防员陈亮为救人牺牲的视频 <|video_begin|> 进行了关注、点赞和长时间观看。这个视频的核心是“家庭责任”与“舍己为人”的奉献精神，是源于真实社会事件的纪实内容。这次主动且深度的互动，标志着用户的兴趣点开始从虚构的戏剧冲突，向更具现实意义和情感共鸣的内容迁移。这不再是简单的娱乐消遣，而是开始被真实世界中的深刻情感所触动。 + +这种兴趣迁移在几天后得到了进一步的印证。在1月29日，用户再次主动关注、点赞并长时观看了一则关于孝心男孩赵文龙为救母徒步求医的视频 <|video_begin|>。这个故事同样聚焦于“家庭责任”和“孝道”这类具有强烈情感冲击力和正向社会价值的主题。连续两次对同类型真实感人故事的深度互动，让我推断用户的需求已经发生了质变。他不再满足于短剧带来的浅层娱乐，而是开始主动寻找能够引发深度情感共鸣、传递正能量的内容，以获得情感上的慰藉和价值上的认同。 + +综合来看，我认为用户的行为路径清晰地展现了一个从被动娱乐到主动寻求情感慰藉的演变过程。最初，他被动地消费大量以“情感纠葛”和“逆袭”为卖点的短剧广告，满足基本的娱乐需求。随后，他开始主动关注并深度互动关于“家庭责任”与“牺牲奉献”的真实社会纪实内容，显示出其兴趣点已从戏剧化冲突转向了对真实情感的共鸣。这一系列行为表明，用户的内容消费动机已经从单纯的消遣，升级为寻求更深层次的情感慰藉和正向价值的确认。因此，我倾向于认为，用户未来可能会对传递亲情、奉献、孝道等主题的真实感人故事或纪实类内容有更高的偏好。 + +我梳理出的行为逻辑链如下： +用户首先深度转化了以家庭关系和情感纠葛为主题的短剧广告，如《重回黄金九八:从逆袭到巅峰》<|ad_begin|>，这表明其对该类娱乐内容有初步兴趣。随后，他的行为从被动接收转向主动互动，关注、点赞并长时观看了关于消防员为家庭责任而牺牲的真实纪实视频 <|video_begin|>，兴趣点从戏剧冲突深化为真实情感共鸣。最后，他再次主动关注并深度互动了另一则关于孝心男孩为母求医的感人故事 <|video_begin|>，这确认了他的兴趣已聚焦于“亲情奉献”这一更具正能量的主题，完成了从消遣娱乐到主动寻求情感慰藉的转变。 +{"logic_chain": {"name": "从被动娱乐到主动寻求情感慰藉的路径探索", "events": [{"date": "2025-12-22", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户被动接收并深度转化了大量以“家庭关系”、“情感纠葛”、“逆袭”为主题的短剧广告，表明其对该类内容存在初步兴趣。"}, {"date": "2026-01-22", "action": "[视频-关注/点赞/长播] <|video_begin|>", "logic": "兴趣深化与行为转变：用户从观看虚构的短剧，转向主动关注、点赞并长时观看真实社会事件中关于“家庭责任”与“牺牲”的纪实内容。行为从被动接收广告转为主动深度互动，兴趣点从“戏剧冲突”深化为“真实情感共鸣”。"}, {"date": "2026-01-29", "action": "[视频-关注/点赞/长播] <|video_begin|>", "logic": "主题聚焦与认知确认：用户再次主动关注并深度互动另一则关于“家庭责任”与“孝道”的真实感人故事。这确认了其兴趣已从宽泛的“情感纠葛”收敛至更具正能量和社会价值的“亲情奉献”主题，完成了从消遣娱乐到寻求情感慰藉和正向价值认同的认知跃迁。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从冲动购买到系统化养宠的认知升级", "events": [{"date": "2026-01-12", "action": "[商品-购买] <|prod_begin|>", "logic": "初始触发：用户购买了活体宠物（鹦鹉），这是整个兴趣链的起点和核心问题源。"}, {"date": "2026-01-13", "action": "[商品-购买] <|prod_begin|>", "logic": "问题解决与工具升级：在饲养一天后，用户迅速意识到手动喂食可能存在不便或浪费（防撒防溅），立即购买了自动化、功能性的喂食设备。这是从基础饲养向工具化、效率化管理的第一次认知升级。"}, {"date": "2026-01-14", "action": "[商品-购买] <|prod_begin|>", "logic": "需求深化（从主食到零食）：用户在解决了基础喂食问题后，开始关注更精细化的需求，如“磨牙”、“训练奖励”。这表明用户的认知已从“喂饱”深化到“互动与健康”，增加了新的养宠维度。"}, {"date": "2026-01-15", "action": "[商品-购买] <|prod_begin|>", "logic": "认知跃迁（从食物到营养补剂）：用户的关注点从可见的食物（主食、零食）跃迁至不可见的微量元素层面（电解质、维生素）。这标志着用户开始系统性地考虑宠物的全面健康和营养均衡，是养宠认知从经验层面到科学层面的关键一步。"}]}}<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-06 01:00:48,257 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 01:00:48,258 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +[INFO|2026-07-06 01:00:48] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-06 01:00:48] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-06 01:00:48,970 >> loading weights file model.safetensors from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-06 01:00:48,971 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-06 01:00:49,023 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file generation_config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-06 01:00:49,809 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-06 01:00:50,211 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-06 01:00:50] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-06 01:00:50] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-06 01:00:50] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-06 01:00:50] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-06 01:00:50] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-06 01:00:50,237 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}. +[INFO|trainer.py:1469] 2026-07-06 01:00:50,343 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-06 01:00:50,343 >> Num examples = 1,592 +[INFO|trainer.py:1471] 2026-07-06 01:00:50,343 >> Num Epochs = 2 +[INFO|trainer.py:1472] 2026-07-06 01:00:50,343 >> Num update steps per epoch = 398 +[INFO|trainer.py:1473] 2026-07-06 01:00:50,343 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-06 01:00:50,343 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-06 01:00:50,343 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-06 01:00:50,343 >> Total optimization steps = 796 +[INFO|trainer.py:1479] 2026-07-06 01:00:50,344 >> Number of trainable parameters = 801,433,600 + 0%| | 0/796 [00:00> `use_return_dict` is deprecated! 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/home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-500/generation_config.json +{'loss': '1.593', 'grad_norm': '1.891', 'learning_rate': '5.224e-06', 'epoch': '1.005'} +{'loss': '1.6', 'grad_norm': '2.922', 'learning_rate': '5.122e-06', 'epoch': '1.018'} +{'loss': '1.535', 'grad_norm': '2.422', 'learning_rate': '5.02e-06', 'epoch': '1.03'} +{'loss': '1.563', 'grad_norm': '2.031', 'learning_rate': '4.919e-06', 'epoch': '1.043'} +{'loss': '1.523', 'grad_norm': '2.594', 'learning_rate': '4.817e-06', 'epoch': '1.055'} +{'loss': '1.578', 'grad_norm': '2.344', 'learning_rate': '4.715e-06', 'epoch': '1.068'} +{'loss': '1.549', 'grad_norm': '2.047', 'learning_rate': '4.614e-06', 'epoch': '1.08'} +{'loss': '1.531', 'grad_norm': '2.594', 'learning_rate': '4.512e-06', 'epoch': '1.093'} +{'loss': '1.542', 'grad_norm': '2.266', 'learning_rate': '4.411e-06', 'epoch': '1.106'} +{'loss': '1.518', 'grad_norm': '2.672', 'learning_rate': '4.31e-06', 'epoch': '1.118'} +{'loss': '1.553', 'grad_norm': '2.516', 'learning_rate': '4.21e-06', 'epoch': '1.131'} +{'loss': '1.566', 'grad_norm': '3.156', 'learning_rate': '4.11e-06', 'epoch': '1.143'} +{'loss': '1.543', 'grad_norm': '2.141', 'learning_rate': '4.01e-06', 'epoch': '1.156'} +{'loss': '1.606', 'grad_norm': '2.531', 'learning_rate': '3.91e-06', 'epoch': '1.168'} +{'loss': '1.522', 'grad_norm': '2.062', 'learning_rate': '3.811e-06', 'epoch': '1.181'} +{'loss': '1.672', 'grad_norm': '2.172', 'learning_rate': '3.712e-06', 'epoch': '1.193'} +{'loss': '1.535', 'grad_norm': '2.734', 'learning_rate': '3.614e-06', 'epoch': '1.206'} +{'loss': '1.621', 'grad_norm': '2.547', 'learning_rate': '3.517e-06', 'epoch': '1.219'} +{'loss': '1.578', 'grad_norm': '2.109', 'learning_rate': '3.42e-06', 'epoch': '1.231'} +{'loss': '1.633', 'grad_norm': '1.852', 'learning_rate': '3.324e-06', 'epoch': '1.244'} +{'loss': '1.543', 'grad_norm': '2.141', 'learning_rate': '3.228e-06', 'epoch': '1.256'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-500/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 02:08:08,156 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-500/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 02:08:08,179 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-500/tokenizer_config.json + 63%|██████▎ | 501/796 [1:07:27<43:33, 8.86s/it] 63%|██████▎ | 502/796 [1:07:35<41:53, 8.55s/it] 63%|██████▎ | 503/796 [1:07:43<40:48, 8.36s/it] 63%|██████▎ | 504/796 [1:07:51<39:42, 8.16s/it] 63%|██████▎ | 505/796 [1:07:59<39:26, 8.13s/it] 63%|██████▎ | 505/796 [1:07:59<39:26, 8.13s/it] 64%|██████▎ | 506/796 [1:08:06<38:50, 8.04s/it] 64%|██████▎ | 507/796 [1:08:14<38:35, 8.01s/it] 64%|██████▍ | 508/796 [1:08:22<38:17, 7.98s/it] 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'grad_norm': '2.25', 'learning_rate': '1.048e-06', 'epoch': '1.595'} +{'loss': '1.537', 'grad_norm': '2.156', 'learning_rate': '9.862e-07', 'epoch': '1.608'} +{'loss': '1.579', 'grad_norm': '2.297', 'learning_rate': '9.264e-07', 'epoch': '1.621'} +{'loss': '1.568', 'grad_norm': '2.953', 'learning_rate': '8.683e-07', 'epoch': '1.633'} +{'loss': '1.529', 'grad_norm': '2.266', 'learning_rate': '8.118e-07', 'epoch': '1.646'} +{'loss': '1.545', 'grad_norm': '1.906', 'learning_rate': '7.571e-07', 'epoch': '1.658'} +{'loss': '1.52', 'grad_norm': '3.047', 'learning_rate': '7.042e-07', 'epoch': '1.671'} +{'loss': '1.557', 'grad_norm': '1.898', 'learning_rate': '6.53e-07', 'epoch': '1.683'} +{'loss': '1.555', 'grad_norm': '2.172', 'learning_rate': '6.037e-07', 'epoch': '1.696'} +{'loss': '1.535', 'grad_norm': '1.922', 'learning_rate': '5.561e-07', 'epoch': '1.709'} +{'loss': '1.529', 'grad_norm': '2.047', 'learning_rate': '5.104e-07', 'epoch': '1.721'} +{'loss': '1.645', 'grad_norm': '1.969', 'learning_rate': '4.666e-07', 'epoch': '1.734'} +{'loss': '1.554', 'grad_norm': '2.656', 'learning_rate': '4.246e-07', 'epoch': '1.746'} +{'loss': '1.594', 'grad_norm': '2.297', 'learning_rate': '3.845e-07', 'epoch': '1.759'} +{'loss': '1.599', 'grad_norm': '2.266', 'learning_rate': '3.463e-07', 'epoch': '1.771'} +{'loss': '1.514', 'grad_norm': '2.5', 'learning_rate': '3.101e-07', 'epoch': '1.784'} +{'loss': '1.644', 'grad_norm': '2.297', 'learning_rate': '2.758e-07', 'epoch': '1.796'} +{'loss': '1.525', 'grad_norm': '1.969', 'learning_rate': '2.435e-07', 'epoch': '1.809'} +{'loss': '1.606', 'grad_norm': '2.234', 'learning_rate': '2.131e-07', 'epoch': '1.822'} +{'loss': '1.575', 'grad_norm': '2.391', 'learning_rate': '1.847e-07', 'epoch': '1.834'} +{'loss': '1.589', 'grad_norm': '2.188', 'learning_rate': '1.583e-07', 'epoch': '1.847'} +{'loss': '1.526', 'grad_norm': '2.203', 'learning_rate': '1.339e-07', 'epoch': '1.859'} +{'loss': '1.514', 'grad_norm': '2.078', 'learning_rate': '1.115e-07', 'epoch': '1.872'} +{'loss': '1.556', 'grad_norm': '2.297', 'learning_rate': '9.118e-08', 'epoch': '1.884'} +{'loss': '1.539', 'grad_norm': '2.531', 'learning_rate': '7.285e-08', 'epoch': '1.897'} +{'loss': '1.509', 'grad_norm': '2.281', 'learning_rate': '5.657e-08', 'epoch': '1.91'} +{'loss': '1.517', 'grad_norm': '2.172', 'learning_rate': '4.233e-08', 'epoch': '1.922'} +{'loss': '1.552', 'grad_norm': '2.828', 'learning_rate': '3.015e-08', 'epoch': '1.935'} +{'loss': '1.529', 'grad_norm': '2.625', 'learning_rate': '2.002e-08', 'epoch': '1.947'} +{'loss': '1.541', 'grad_norm': '2.062', 'learning_rate': '1.196e-08', 'epoch': '1.96'} +{'loss': '1.507', 'grad_norm': '2.156', 'learning_rate': '5.96e-09', 'epoch': '1.972'} +{'loss': '1.575', 'grad_norm': '2.391', 'learning_rate': '2.028e-09', 'epoch': '1.985'} +{'loss': '1.56', 'grad_norm': '2.172', 'learning_rate': '1.656e-10', 'epoch': '1.997'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-796/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 02:47:45,316 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-796/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 02:47:45,336 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/checkpoint-796/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-06 02:47:47,286 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 796/796 [1:46:56<00:00, 8.25s/it] 100%|██████████| 796/796 [1:46:56<00:00, 8.06s/it] +[INFO|trainer.py:3815] 2026-07-06 02:47:47,287 >> Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2 +[INFO|configuration_utils.py:533] 2026-07-06 02:47:47,288 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/config.json +[INFO|configuration_utils.py:816] 2026-07-06 02:47:47,288 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/generation_config.json +{'train_runtime': '6417', 'train_samples_per_second': '0.496', 'train_steps_per_second': '0.124', 'train_loss': '1.645', 'epoch': '2'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 02:47:48,114 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 02:47:48,135 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/tokenizer_config.json +***** train metrics ***** + epoch = 2.0 + total_flos = 356982409GF + train_loss = 1.6446 + train_runtime = 1:46:56.94 + train_samples_per_second = 0.496 + train_steps_per_second = 0.124 +Figure saved at: /home/ll/llm4rec/experiments/20260706_overnight/outputs/v03_all_lr1e5_ep2/training_loss.png +[WARNING|2026-07-06 02:47:48] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-06 02:47:48] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-06 02:47:48,237 >> Dropping the following result as it does not have all the necessary fields: 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