From 7adeacde43fa9457d0d9abc1cf788a32a2a4542e Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 29 Jul 2026 10:19:17 +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/v10_balanced_core_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 + ...84875.ubuntu-System-Product-Name.1707657.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2434 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 246 ++ trainer_state.json | 1758 ++++++++++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 51215 bytes 16 files changed, 4740 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_04-54-35_ubuntu-System-Product-Name/events.out.tfevents.1783284875.ubuntu-System-Product-Name.1707657.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text 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filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..77719b8 --- /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: v10_balanced_core_lr1e5_ep2 + results: [] +--- + + + +# v10_balanced_core_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_balanced_core 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: 202607070 +- 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..776b49e --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 2.0, + "total_flos": 5.923294725916508e+17, + "train_loss": 1.5350287483722507, + "train_runtime": 10749.7665, + "train_samples_per_second": 0.456, + "train_steps_per_second": 0.114 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..419bcb5 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fecf75870ba8a20b69957401afdeadf7e99aad62b9424e38b55353c3b6e07ae +size 1602902832 diff --git a/runs/Jul06_04-54-35_ubuntu-System-Product-Name/events.out.tfevents.1783284875.ubuntu-System-Product-Name.1707657.0 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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..a2dc007 --- /dev/null +++ b/train.log @@ -0,0 +1,2434 @@ +/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 04:54:01] 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 04:54:01,789 >> 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 04:54:01,793 >> 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 04:54:05,566 >> 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 04:54:05,567 >> 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 04:54:05,934 >> 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 04:54:05,935 >> 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 04:54:07] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/20260706_overnight/data/balanced_core.jsonl... + Converting format of dataset (num_proc=16): 100%|██████████| 33952/33952 [00:00user +【用户交互历史】： +【2025-01-02】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-01-16】 + --:-- [直播-关注] <|living_begin|> +【2025-01-22】 + --:-- [直播-关注] <|living_begin|> +【2025-01-24】 + --:-- [直播-关注] <|living_begin|> +【2025-01-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-02-04】 + --:-- [直播-关注] <|living_begin|> +【2025-02-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-02-08】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-打赏] <|living_begin|> +【2025-02-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-02-24】 + --:-- [直播-关注/打赏] <|living_begin|> +【2025-03-01】 + --:-- [直播-关注] <|living_begin|> +【2025-03-02】 + --:-- [直播-关注] <|living_begin|> +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> +【2025-03-24】 + --:-- [直播-打赏] <|living_begin|> +【2025-03-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-03-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [直播-关注] <|living_begin|> +【2025-03-29】 + --:-- [直播-关注] <|living_begin|> +【2025-03-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-09】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-11】 + --:-- [直播-关注] <|living_begin|> +【2025-04-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-14】 + --:-- [直播-打赏] <|living_begin|> +【2025-04-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-17】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-07】 + --:-- [直播-关注] <|living_begin|> +【2025-05-13】 + --:-- [直播-关注] <|living_begin|> +【2025-05-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [直播-关注] <|living_begin|> +【2025-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-05】 + --:-- [直播-关注] <|living_begin|> +【2025-06-06】 + --:-- [直播-打赏] <|living_begin|> +【2025-06-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-30】 + --:-- [直播-关注] <|living_begin|> +【2025-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-08】 + --:-- [商品-购买] <|prod_begin|> 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+【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-17】 + --:-- [直播-关注] <|living_begin|> +【2025-10-03】 + --:-- [直播-关注] <|living_begin|> +【2025-10-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [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|> +【2025-11-18】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-29】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 03:03 [广告-深度转化] <|ad_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|> + 10:11 [广告-深度转化] <|ad_begin|> + 12:14 [广告-深度转化] <|ad_begin|> + 13:49 [广告-深度转化] <|ad_begin|> + 14:25 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + 14:35 [广告-深度转化] <|ad_begin|> + 17:18 [广告-深度转化] <|ad_begin|> + 17:56 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:11 [视频-关注/长播] <|video_begin|> + 01:14 [视频-评论/点赞/长播] <|video_begin|> + 02:47 [视频-评论/点赞/长播] <|video_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 07:14 [广告-深度转化] <|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|> + 09:55 [广告-深度转化] <|ad_begin|> + 13:35 [广告-深度转化] <|ad_begin|> + 16:06 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + 02:54 [视频-评论/点赞/长播] <|video_begin|> + 07:03 [广告-深度转化] <|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|> + 08:15 [广告-深度转化] <|ad_begin|> + 11:49 [视频-评论/点赞/长播] <|video_begin|> + 12:51 [视频-评论/点赞/长播] <|video_begin|> + 13:48 [视频-评论/点赞/长播] <|video_begin|> + 14:08 [广告-深度转化] <|ad_begin|> + 14:39 [视频-评论/点赞/长播] <|video_begin|> + 14:59 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 01:37 [视频-评论/点赞/长播] <|video_begin|> + 01:50 [广告-深度转化] <|ad_begin|> + 05:45 [视频-评论/点赞/长播] <|video_begin|> + 07:11 [视频-评论/长播] <|video_begin|> + 07:18 [视频-评论/点赞/长播] <|video_begin|> + 07:21 [视频-评论/点赞/长播] <|video_begin|> + 07:33 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:13 [广告-深度转化] <|ad_begin|> + 09:13 [广告-点击] <|ad_begin|> + 11:41 [视频-评论/点赞/长播] <|video_begin|> + 11:44 [广告-点击] <|ad_begin|> + 12:05 [广告-深度转化] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> + 14:29 [广告-点击] <|ad_begin|> +【2026-01-25】 + 07:03 [视频-评论/点赞/长播] <|video_begin|> + 07:12 [广告-深度转化] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 10:03 [广告-点击] <|ad_begin|> + 10:33 [广告-点击] <|ad_begin|> + 15:40 [广告-点击] <|ad_begin|> + 15:58 [广告-深度转化] <|ad_begin|> + 15:58 [广告-点击] <|ad_begin|> +【2026-01-26】 + 01:03 [广告-深度转化] <|ad_begin|> + 01:03 [广告-点击] <|ad_begin|> + 06:10 [视频-关注/长播] <|video_begin|> + 06:10 [视频-关注/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 10:27 [广告-点击] <|ad_begin|> + 15:01 [广告-深度转化] <|ad_begin|> + 15:01 [广告-点击] <|ad_begin|> + 17:02 [广告-深度转化] <|ad_begin|> + 17:02 [广告-点击] <|ad_begin|> +【2026-01-27】 + 01:21 [广告-深度转化] <|ad_begin|> + 01:21 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:29 [视频-关注/长播] <|video_begin|> + 09:29 [视频-评论/点赞/长播] <|video_begin|> + 09:33 [视频-关注/长播] <|video_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 09:45 [广告-点击] <|ad_begin|> + 11:08 [视频-关注/长播] <|video_begin|> + 11:54 [视频-关注/长播] <|video_begin|> + 13:44 [广告-深度转化] <|ad_begin|> + 13:44 [广告-点击] <|ad_begin|> + 16:27 [视频-评论/点赞/长播] <|video_begin|> + 16:50 [广告-深度转化] <|ad_begin|> + 16:50 [广告-点击] <|ad_begin|> +【2026-01-28】 + 01:36 [广告-深度转化] <|ad_begin|> + 01:36 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:03 [广告-深度转化] <|ad_begin|> + 12:03 [广告-点击] <|ad_begin|> + 13:55 [广告-深度转化] <|ad_begin|> + 13:55 [广告-点击] <|ad_begin|> + 15:42 [广告-深度转化] <|ad_begin|> + 15:42 [广告-点击] <|ad_begin|> + 22:29 [广告-深度转化] <|ad_begin|> + 22:29 [广告-点击] <|ad_begin|> +【2026-01-29】 + 01:38 [视频-评论/点赞/长播] <|video_begin|> + 07:45 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:16 [视频-关注/长播] <|video_begin|> + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> + 12:38 [视频-点赞/长播] <|video_begin|> + 12:42 [视频-点赞/长播] <|video_begin|> + 12:45 [视频-点赞/长播] <|video_begin|> + 12:52 [广告-深度转化] <|ad_begin|> + 12:52 [广告-点击] <|ad_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 15:31 [视频-评论/点赞/长播] <|video_begin|> + 15:31 [视频-评论/点赞/长播] <|video_begin|> + 15:45 [广告-深度转化] <|ad_begin|> + 15:45 [广告-点击] <|ad_begin|> +【2026-01-30】 + 02:30 [广告-深度转化] <|ad_begin|> + 02:30 [广告-点击] <|ad_begin|> + 03:28 [视频-点赞/长播] <|video_begin|> + 10:44 [广告-深度转化] <|ad_begin|> + 10:44 [广告-点击] <|ad_begin|> + 13:11 [视频-评论/长播] <|video_begin|> + 13:28 [视频-点赞/长播] <|video_begin|> + 13:32 [视频-点赞/长播] <|video_begin|> + 13:35 [视频-点赞/长播] <|video_begin|> + 15:46 [视频-点赞/长播] <|video_begin|> + 15:46 [视频-评论/长播] <|video_begin|> + 15:46 [视频-评论/长播] <|video_begin|> + 15:58 [广告-深度转化] <|ad_begin|> + 15:58 [广告-点击] <|ad_begin|> + 19:16 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:59 [视频-评论/点赞/长播] <|video_begin|> + 09:41 [广告-深度转化] <|ad_begin|> + 09:41 [广告-点击] <|ad_begin|> + 10:52 [视频-点赞/长播] <|video_begin|> + 11:13 [广告-深度转化] <|ad_begin|> + 11:13 [广告-点击] <|ad_begin|> + 16:24 [视频-点赞/长播] <|video_begin|> + 16:40 [视频-点赞/长播] <|video_begin|> + 16:40 [视频-评论/点赞/长播] <|video_begin|> + 16:47 [视频-点赞/长播] <|video_begin|> + 17:05 [广告-深度转化] <|ad_begin|> + 17:05 [广告-点击] <|ad_begin|> + 18:50 [广告-深度转化] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> +【2026-02-01】 + 01:57 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 03:41 [广告-深度转化] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:28 [视频-评论/长播] <|video_begin|> + 08:40 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:54 [广告-点击] <|ad_begin|> + 09:50 [视频-点赞/长播] <|video_begin|> + 09:52 [视频-点赞/长播] <|video_begin|> + 09:52 [视频-点赞/长播] <|video_begin|> + 12:23 [视频-点赞/长播] <|video_begin|> + 16:01 [视频-点赞/长播] <|video_begin|> + 16:06 [视频-评论/点赞/长播] <|video_begin|> + 16:08 [视频-点赞/长播] <|video_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 16:28 [广告-点击] <|ad_begin|> + 17:15 [视频-评论/点赞/长播] <|video_begin|> + 17:18 [视频-评论/点赞/长播] <|video_begin|> +【2026-02-02】 + 00:22 [广告-深度转化] <|ad_begin|> + 00:22 [广告-点击] <|ad_begin|> + 02:20 [视频-点赞/长播] <|video_begin|> + 02:20 [视频-点赞/长播] <|video_begin|> + 02:20 [视频-关注/评论/点赞/长播] <|video_begin|> + 05:19 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-评论/点赞/长播] <|video_begin|> + 05:23 [视频-点赞/长播] <|video_begin|> + 05:35 [视频-点赞/长播] <|video_begin|> + 05:42 [视频-点赞/长播] <|video_begin|> + 05:42 [视频-评论/点赞/长播] <|video_begin|> + 05:54 [视频-点赞/长播] <|video_begin|> + 05:54 [视频-评论/点赞/长播] <|video_begin|> + 05:56 [视频-点赞/长播] <|video_begin|> + 06:03 [视频-点赞/长播] <|video_begin|> + 07:27 [视频-点赞/长播] <|video_begin|> + 07:31 [广告-点击] <|ad_begin|> + 07:31 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:00 [视频-点赞/长播] <|video_begin|> + 08:10 [视频-点赞/长播] <|video_begin|> + 08:11 [视频-长播] <|video_begin|> + 08:11 [视频-评论/点赞/长播] <|video_begin|> + 08:14 [广告-点击] <|ad_begin|> + 08:16 [视频-点赞/长播] <|video_begin|> + 09:23 [视频-点赞/长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:28 [视频-点赞/长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:33 [视频-长播] <|video_begin|> + 09:33 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:35 [视频-点赞/长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 10:00 [视频-点赞/长播] <|video_begin|> + 10:15 [广告-深度转化] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 12:51 [广告-深度转化] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + 14:42 [广告-深度转化] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:46 [广告-深度转化] <|ad_begin|> + 00:46 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:25 [视频-点赞/长播] <|video_begin|> + 08:25 [视频-评论/点赞/长播] <|video_begin|> + 08:27 [广告-点击] <|ad_begin|> + 08:30 [广告-深度转化] <|ad_begin|> + 08:30 [广告-点击] <|ad_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:47 [视频-评论/点赞/长播] <|video_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 11:14 [视频-点赞/长播] <|video_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:48 [广告-深度转化] <|ad_begin|> + 11:48 [广告-点击] <|ad_begin|> + 13:51 [视频-评论/点赞/长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> + 13:56 [视频-点赞/长播] <|video_begin|> + 13:56 [视频-评论/点赞/长播] <|video_begin|> + 15:54 [广告-深度转化] <|ad_begin|> + 15:54 [广告-点击] <|ad_begin|> + 16:39 [广告-深度转化] <|ad_begin|> + 16:39 [广告-点击] <|ad_begin|> +【2026-02-04】 + 00:36 [视频-长播] <|video_begin|> + 01:54 [广告-深度转化] <|ad_begin|> + 01:54 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:52 [视频-长播] <|video_begin|> + 08:52 [视频-点赞/长播] <|video_begin|> + 09:04 [视频-评论/点赞/长播] <|video_begin|> + 09:12 [广告-点击] <|ad_begin|> + 09:25 [广告-深度转化] <|ad_begin|> + 09:25 [广告-点击] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> + 11:27 [广告-深度转化] <|ad_begin|> + 11:27 [广告-点击] <|ad_begin|> + 13:00 [广告-深度转化] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> + 13:29 [广告-点击] <|ad_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> + 14:04 [广告-深度转化] <|ad_begin|> + 14:04 [广告-点击] <|ad_begin|> + 14:30 [广告-点击] <|ad_begin|> + 14:32 [视频-长播] <|video_begin|> + 15:15 [广告-深度转化] <|ad_begin|> + 15:15 [广告-点击] <|ad_begin|> + 17:28 [广告-深度转化] <|ad_begin|> + 17:28 [广告-点击] <|ad_begin|> + 18:02 [视频-长播] <|video_begin|> + 18:02 [视频-点赞/长播] <|video_begin|> + 18:06 [视频-长播] <|video_begin|> + 18:06 [视频-长播] <|video_begin|> + 18:06 [视频-长播] <|video_begin|> + 18:06 [视频-点赞/长播] <|video_begin|> + 18:11 [视频-长播] <|video_begin|> + 18:11 [视频-点赞/长播] <|video_begin|> + 18:11 [视频-点赞/长播] <|video_begin|> + 18:17 [广告-点击] <|ad_begin|> +【2026-02-05】 + 01:34 [视频-长播] <|video_begin|> + 01:34 [视频-评论/点赞/长播] <|video_begin|> + 03:30 [广告-深度转化] <|ad_begin|> + 03:30 [广告-点击] <|ad_begin|> + 08:48 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> + 09:01 [视频-长播] <|video_begin|> + 09:01 [视频-长播] <|video_begin|> + 09:04 [广告-点击] <|ad_begin|> + 09:06 [视频-长播] <|video_begin|> + 09:06 [视频-长播] <|video_begin|> + 09:08 [视频-长播] <|video_begin|> + 09:08 [视频-长播] <|video_begin|> + 09:08 [视频-长播] <|video_begin|> + 09:11 [视频-长播] <|video_begin|> + 09:11 [视频-长播] <|video_begin|> + 09:11 [视频-长播] <|video_begin|> + 09:16 [视频-长播] <|video_begin|> + 09:16 [视频-长播] <|video_begin|> + 09:16 [视频-长播] <|video_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:30 [视频-点赞/长播] <|video_begin|> + 09:33 [视频-点赞/长播] <|video_begin|> + 09:33 [视频-评论/点赞/长播] <|video_begin|> + 09:47 [广告-深度转化] <|ad_begin|> + 09:47 [广告-点击] <|ad_begin|> + 12:12 [视频-长播] <|video_begin|> + 12:12 [视频-长播] <|video_begin|> + 12:12 [视频-长播] <|video_begin|> + 12:19 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-评论/点赞/长播] <|video_begin|> + 13:36 [视频-点赞/长播] <|video_begin|> + 13:39 [广告-点击] <|ad_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-点赞/长播] <|video_begin|> + 14:10 [广告-深度转化] <|ad_begin|> + 14:10 [广告-点击] <|ad_begin|> + +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题，严格提取出所有相关的历史交互条目。 + +主题：从泛兴趣到专业化探索：玉石文玩认知路径 + +输出格式要求：请严格只输出包含 SID 字段的 JSON 数组格式，禁止输出任何其他补充说明或解释性文本。/no_think<|im_end|> +<|im_start|>assistant + + +["<|living_begin|>", "<|living_begin|>", "<|living_begin|>", "<|living_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-03-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-12】 + --:-- [直播-关注] <|living_begin|> +【2025-06-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-06-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-17】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + 14:59 [广告-点击] <|ad_begin|> +【2025-12-14】 + 00:36 [广告-点击] <|ad_begin|> + 04:06 [广告-点击] <|ad_begin|> + 09:35 [广告-点击] <|ad_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> + 04:39 [广告-点击] <|ad_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + 12:07 [广告-点击] <|ad_begin|> + 19:01 [广告-点击] <|ad_begin|> +【2025-12-28】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [商品-加购] <|prod_begin|> +【2025-12-30】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2025-12-31】 + 19:36 [广告-深度转化] <|ad_begin|> + 19:36 [广告-点击] <|ad_begin|> +【2026-01-02】 + 03:35 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> + 09:11 [广告-点击] <|ad_begin|> + 16:47 [广告-深度转化] <|ad_begin|> + 16:47 [广告-点击] <|ad_begin|> +【2026-01-05】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-07】 + --:-- [商品-加购] <|prod_begin|> + 13:22 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + --:-- [商品-加购] <|prod_begin|> + 07:01 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 04:38 [视频-转发/点赞/长播] <|video_begin|> + 04:59 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-23】 + 03:59 [视频-转发/点赞/长播] <|video_begin|> + 04:03 [视频-转发/点赞/长播] <|video_begin|> + 04:03 [视频-转发/点赞/长播] <|video_begin|> + 04:14 [视频-转发/点赞/长播] <|video_begin|> + 04:24 [视频-转发/点赞/长播] <|video_begin|> + 05:01 [视频-转发/长播] <|video_begin|> + 05:38 [视频-转发/长播] <|video_begin|> + 05:40 [视频-转发/长播] <|video_begin|> + 07:11 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-24】 + 04:40 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 07:12 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-26】 + 00:59 [视频-转发/点赞/长播] <|video_begin|> + 02:11 [视频-转发/长播] <|video_begin|> + 02:36 [视频-转发/点赞/长播] <|video_begin|> + 05:33 [视频-转发/点赞/长播] <|video_begin|> + 05:48 [视频-转发/长播] <|video_begin|> + 08:02 [视频-转发/点赞/长播] <|video_begin|> + 09:39 [视频-转发/点赞/长播] <|video_begin|> + 09:41 [视频-转发/点赞/长播] <|video_begin|> + 09:46 [视频-转发/点赞/长播] <|video_begin|> + 14:19 [视频-转发/点赞/长播] <|video_begin|> + 14:19 [视频-转发/长播] <|video_begin|> + 14:59 [视频-转发/长播] <|video_begin|> + 15:06 [视频-转发/长播] <|video_begin|> + 15:16 [视频-转发/长播] <|video_begin|> + 15:38 [视频-转发/长播] <|video_begin|> +【2026-01-27】 + 04:24 [视频-转发/长播] <|video_begin|> + 04:56 [视频-转发/长播] <|video_begin|> + 05:30 [广告-点击] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + 09:16 [视频-转发/点赞/长播] <|video_begin|> + 09:28 [视频-转发/点赞/长播] <|video_begin|> + 10:24 [视频-收藏/点赞/长播] <|video_begin|> + 11:09 [广告-点击] <|ad_begin|> + 13:05 [视频-收藏/点赞/长播] <|video_begin|> + 13:25 [视频-转发/长播] <|video_begin|> + 13:27 [视频-转发/长播] <|video_begin|> + 13:33 [视频-转发/点赞/长播] <|video_begin|> + 13:33 [视频-转发/收藏/点赞/长播] <|video_begin|> + 13:36 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 01:12 [视频-转发/点赞/长播] <|video_begin|> + 01:21 [视频-收藏/点赞/长播] <|video_begin|> + 01:21 [视频-收藏/点赞/长播] <|video_begin|> + 01:21 [视频-转发/收藏/点赞/长播] <|video_begin|> + 01:45 [视频-转发/收藏/点赞/长播] <|video_begin|> + 01:59 [视频-转发/收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [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|> + 03:58 [视频-收藏/点赞/长播] <|video_begin|> + 03:58 [视频-收藏/点赞/长播] <|video_begin|> + 07:16 [视频-收藏/点赞/长播] <|video_begin|> + 07:24 [视频-收藏/点赞/长播] <|video_begin|> + 07:27 [视频-收藏/点赞/长播] <|video_begin|> + 07:38 [视频-收藏/点赞/长播] <|video_begin|> + 07:42 [视频-收藏/点赞/长播] <|video_begin|> + 07:42 [视频-收藏/点赞/长播] <|video_begin|> + 07:49 [视频-收藏/点赞/长播] <|video_begin|> + 11:53 [视频-收藏/点赞/长播] <|video_begin|> + 11:53 [视频-收藏/点赞/长播] <|video_begin|> + 15:14 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-29】 + 01:30 [视频-收藏/点赞/长播] <|video_begin|> + 01:33 [视频-收藏/点赞/长播] <|video_begin|> + 01:42 [视频-收藏/点赞/长播] <|video_begin|> + 01:49 [视频-转发/收藏/点赞/长播] <|video_begin|> + 01:51 [视频-转发/点赞/长播] <|video_begin|> + 01:51 [视频-收藏/点赞/长播] <|video_begin|> + 01:51 [视频-收藏/点赞/长播] <|video_begin|> + 01:54 [视频-转发/收藏/点赞/长播] <|video_begin|> + 01:57 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:03 [视频-收藏/点赞/长播] <|video_begin|> + 02:03 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:06 [视频-收藏/点赞/长播] <|video_begin|> + 02:06 [视频-收藏/长播] <|video_begin|> + 02:06 [视频-收藏/点赞] <|video_begin|> + 02:15 [视频-转发/长播] <|video_begin|> + 02:15 [视频-收藏/点赞/长播] <|video_begin|> + 02:15 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:19 [视频-收藏/点赞/长播] <|video_begin|> + 02:19 [视频-收藏/点赞/长播] <|video_begin|> + 02:19 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:24 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:29 [视频-转发/收藏/点赞/长播] <|video_begin|> + 04:20 [视频-收藏/点赞/长播] <|video_begin|> + 04:24 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-30】 + 00:54 [视频-收藏/点赞/长播] <|video_begin|> + 01:09 [视频-收藏/点赞/长播] <|video_begin|> + 03:47 [广告-点击] <|ad_begin|> + 04:33 [视频-收藏/点赞/长播] <|video_begin|> + 04:33 [视频-收藏/点赞/长播] <|video_begin|> + 04:43 [视频-收藏/点赞/长播] <|video_begin|> + 04:45 [广告-点击] <|ad_begin|> + 06:03 [广告-点击] <|ad_begin|> + 10:48 [视频-收藏/长播] <|video_begin|> + 11:43 [视频-转发/长播] <|video_begin|> + 11:45 [视频-转发/长播] <|video_begin|> + 11:45 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + 09:42 [视频-收藏/点赞/长播] <|video_begin|> + 11:29 [视频-收藏/点赞/长播] <|video_begin|> + 11:45 [视频-转发/收藏/点赞/长播] <|video_begin|> + 11:57 [视频-转发/收藏/点赞/长播] <|video_begin|> + 14:27 [广告-点击] <|ad_begin|> + 14:36 [视频-收藏/点赞/长播] <|video_begin|> + 19:09 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:02 [视频-收藏/点赞/长播] <|video_begin|> + 06:09 [视频-收藏/点赞/长播] <|video_begin|> + 15:57 [视频-转发/收藏/点赞/长播] <|video_begin|> +【2026-02-02】 + 01:27 [视频-收藏/点赞/长播] <|video_begin|> + 01:27 [视频-收藏/点赞/长播] <|video_begin|> + 01:27 [视频-收藏/点赞/长播] <|video_begin|> + 02:38 [视频-转发/收藏/点赞/长播] <|video_begin|> + 02:39 [视频-转发/收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:32 [视频-转发/收藏/点赞/长播] <|video_begin|> + 09:35 [视频-收藏/点赞/长播] <|video_begin|> + 18:17 [视频-转发/长播] <|video_begin|> + 18:17 [视频-转发/收藏/点赞/长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + 05:44 [视频-点赞/长播] <|video_begin|> + 05:49 [视频-点赞/长播] <|video_begin|> + 05:49 [视频-点赞/长播] <|video_begin|> + 06:06 [视频-点赞/长播] <|video_begin|> + 06:12 [视频-点赞/长播] <|video_begin|> + 06:12 [视频-点赞/长播] <|video_begin|> + 06:17 [视频-点赞/长播] <|video_begin|> + 08:26 [视频-收藏/点赞/长播] <|video_begin|> + 13:07 [视频-收藏/点赞/长播] <|video_begin|> + 13:20 [广告-点击] <|ad_begin|> +【2026-02-04】 + 01:29 [视频-收藏/点赞/长播] <|video_begin|> + 01:29 [视频-收藏/点赞/长播] <|video_begin|> + 01:35 [视频-点赞/长播] <|video_begin|> + 01:35 [视频-收藏/点赞/长播] <|video_begin|> + 01:42 [视频-点赞/长播] <|video_begin|> + 01:54 [视频-点赞/长播] <|video_begin|> + 01:54 [视频-点赞/长播] <|video_begin|> + 01:54 [视频-点赞/长播] <|video_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|> + 06:01 [视频-点赞/长播] <|video_begin|> + 06:01 [视频-点赞/长播] <|video_begin|> + 09:34 [视频-点赞/长播] <|video_begin|> + 09:36 [广告-点击] <|ad_begin|> + 11:16 [视频-点赞/长播] <|video_begin|> + 11:16 [视频-点赞/长播] <|video_begin|> + 11:16 [视频-转发/长播] <|video_begin|> + 12:06 [视频-点赞/长播] <|video_begin|> + 12:06 [视频-点赞/长播] <|video_begin|> + 12:10 [视频-长播] <|video_begin|> + 12:10 [视频-长播] <|video_begin|> + 12:10 [视频-点赞/长播] <|video_begin|> + 12:16 [视频-长播] <|video_begin|> + 12:16 [视频-长播] <|video_begin|> + 12:16 [视频-长播] <|video_begin|> + 12:16 [视频-点赞/长播] <|video_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:23 [视频-点赞/长播] <|video_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 16:17 [视频-长播] <|video_begin|> + 16:17 [视频-点赞/长播] <|video_begin|> + 16:26 [视频-长播] <|video_begin|> + 16:26 [视频-转发/长播] <|video_begin|> + 16:27 [视频-长播] <|video_begin|> + 16:27 [视频-长播] <|video_begin|> + 16:30 [视频-长播] <|video_begin|> + 16:30 [视频-点赞/长播] <|video_begin|> + 16:34 [视频-长播] <|video_begin|> + 16:37 [视频-长播] <|video_begin|> + 16:37 [视频-点赞/长播] <|video_begin|> + 16:41 [视频-长播] <|video_begin|> + 16:43 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 18:31 [视频-长播] <|video_begin|> + 18:31 [视频-点赞/长播] <|video_begin|> + 18:38 [广告-深度转化] <|ad_begin|> + 18:38 [广告-点击] <|ad_begin|> +【2026-02-05】 + 01:37 [视频-长播] <|video_begin|> + 01:37 [视频-长播] <|video_begin|> + 01:37 [视频-点赞/长播] <|video_begin|> + 01:37 [视频-点赞/长播] <|video_begin|> + 01:40 [广告-点击] <|ad_begin|> + 01:46 [视频-长播] <|video_begin|> + 01:52 [视频-长播] <|video_begin|> + 01:52 [视频-长播] <|video_begin|> + 02:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:44 [视频-点赞/长播] <|video_begin|> + 05:44 [视频-点赞/长播] <|video_begin|> + 06:40 [视频-长播] <|video_begin|> + 06:40 [视频-点赞/长播] <|video_begin|> + 06:40 [视频-点赞/长播] <|video_begin|> + 06:55 [视频-点赞/长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 06:59 [视频-点赞/长播] <|video_begin|> + 07:02 [视频-点赞/长播] <|video_begin|> + 08:20 [视频-点赞/长播] <|video_begin|> + 09:33 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:40 [视频-点赞/长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> + 10:41 [视频-点赞/长播] <|video_begin|> + 10:41 [视频-转发/点赞/长播] <|video_begin|> + 10:53 [视频-长播] <|video_begin|> + 10:53 [视频-点赞/长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 11:01 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 13:06 [视频-点赞/长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:24 [视频-点赞/长播] <|video_begin|> + 13:24 [视频-点赞/长播] <|video_begin|> + 13:33 [视频-长播] <|video_begin|> + 13:33 [视频-长播] <|video_begin|> + 13:40 [视频-长播] <|video_begin|> + 13:46 [视频-点赞/长播] <|video_begin|> + 14:05 [视频-点赞/长播] <|video_begin|> + 14:05 [视频-长播] <|video_begin|> + 14:05 [视频-点赞/长播] <|video_begin|> + 14:07 [视频-长播] <|video_begin|> + 14:07 [视频-点赞/长播] <|video_begin|> + 14:07 [视频-点赞/长播] <|video_begin|> + 14:07 [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）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请针对下列主题提取行为逻辑链： +主题：从泛化娱乐到特定游戏IP的深度沉浸 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化娱乐到特定游戏IP的深度沉浸", + "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，梳理出一条从泛化娱乐兴趣到对特定游戏IP深度沉浸的行为逻辑链。 + +我首先注意到，这位用户的兴趣相当广泛，早期在游戏方面主要集中在《王者荣耀》和《第五人格》上，购买了大量周边商品，例如《王者荣耀》的卡片 <|prod_begin|> 和多次购买《第五人格》的徽章、钥匙扣等 <|prod_begin|>、<|prod_begin|>。这表明他/她是一个典型的二次元和游戏爱好者，对喜爱的IP有很强的付费意愿和收藏偏好。同时，用户的消费行为也展现出对低价、高性价比商品（如各种9.9元商品）和可爱、二次元风格小物件的偏爱。 + +在2026年1月26日之前，用户的视频消费内容非常多样化，涵盖了萌宠、搞笑、社会生活、动漫解说等多个领域，但没有明显集中于某一特定IP。直到1月27日，我观察到一个显著的兴趣转折点。当天，用户首次点击了一则关于《明日方舟》新版本“终末地”的宣传广告 <|ad_begin|>。这次点击似乎是一个关键的触发点，为后续的行为埋下了伏笔。 + +这次广告点击之后，用户的行为立刻显示出对这个新信息的强烈反应。仅仅几个小时后，我就看到他/她开始主动消费与“终末地”相关的深度内容，比如收藏并长时间观看了分析管理员角色背景和世界观设定的视频 <|video_begin|>。这种从被动接收广告信息到主动探索深度内容的转变，并且伴随着收藏和点赞这类高价值互动，清晰地表明用户的兴趣被成功激发，并且正在快速加深。 + +接下来的几天，这种兴趣的深化趋势愈发明显。我发现用户的关注点从“这是个什么故事”迅速升级为“这个游戏该怎么玩”。在1月29日，他/她收藏并观看了介绍“终末地”卡池规则和抽卡策略的视频 <|video_begin|>。这说明用户已经产生了强烈的游玩意愿，开始为进入游戏做准备，研究核心的付费机制和资源分配策略。这种行为已经超越了普通内容消费者的范畴，表现出准玩家的特征。 + +仅仅两天后，在1月31日，用户的探索行为变得更加具体和硬核。他/她开始观看关于游戏内“基建布局”和“材料制作”这类高阶攻略视频 <|video_begin|>。这表明用户的认知已经从“想玩”进化到了“想玩好”，正在预习具体的游戏技巧，希望在游戏开始时就能高效推进。这种对复杂游戏系统的深入研究，标志着其沉浸程度已经达到了一个新高度。 + +最终，在2月5日，我观察到这条兴趣链形成了一个完美的闭环。用户的兴趣从纯粹的线上数字内容，延伸到了与现实生活相关的实体商品。他/她点击了《明日方舟：终末地》的联名交通卡 <|prod_begin|>。点击购买游戏IP的实体周边，尤其是交通卡这种具有身份标识和日常使用属性的物品，强烈地表明了用户对该IP的高度认同感。这不仅是消费行为，更是一种身份的宣告。 + +综合来看，我认为用户的行为清晰地展现了一个从被动接收信息到深度沉浸于特定游戏IP的完整过程。这个过程始于一次广告点击 <|ad_begin|>，随后通过观看角色背景解析视频 <|video_begin|> 来深化兴趣，接着通过研究卡池规则 <|video_begin|> 和基建攻略 <|video_begin|> 为实际游玩做准备，最终通过点击联名实体周边 <|prod_begin|> 完成了从线上兴趣到线下身份认同的转化。基于此，我推测该用户未来有很大概率会成为《明日方舟：终末地》的活跃玩家，并可能继续关注和消费该IP的各类内容与商品。 +{"logic_chain": {"name": "从泛化娱乐到特定游戏IP的深度沉浸", "events": [{"date": "2026-01-27", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：通过广告被动接收到《明日方舟》新版本“终末地”的核心信息，这是兴趣链的起点。"}, {"date": "2026-01-27", "action": "[视频-收藏/点赞/长播] <|video_begin|>", "logic": "兴趣深化与验证：从广告的泛化信息，转向对特定角色背景和世界观设定的深度内容消费，交互从“点击”升级为“收藏/点赞/长播”，表明兴趣被初步确认并加深。"}, {"date": "2026-01-29", "action": "[视频-转发/收藏/点赞/长播] <|video_begin|>", "logic": "认知跃迁与行为准备：兴趣从“了解故事”演变为“研究玩法”，开始关注“卡池规则”、“保底数值”等核心游戏机制和攻略型信息，体现出从旁观者向潜在玩家转化的明确意图。"}, {"date": "2026-01-31", "action": "[视频-收藏/点赞/长播] <|video_begin|>", "logic": "策略收敛与技能预习：进一步聚焦于更具体的“基建布局”、“材料制作”等高阶游戏攻略，表明用户不仅想玩，而且希望高效地玩好，认知深度已达到资深玩家水平。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "兴趣闭环与身份认同：用户的兴趣从线上内容消费延伸至线下实体周边，点击联名交通卡这一行为，标志着其对该游戏IP的认同感已经形成，完成了从“了解”到“认同”的完整认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-10】 + --:-- [直播-关注] <|living_begin|> +【2025-01-27】 + --:-- [直播-关注] <|living_begin|> +【2025-02-12】 + --:-- [直播-关注] <|living_begin|> +【2025-02-21】 + --:-- [直播-关注] <|living_begin|> +【2025-02-28】 + --:-- [直播-关注] <|living_begin|> +【2025-03-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [直播-关注] <|living_begin|> +【2025-04-04】 + --:-- [直播-关注] <|living_begin|> +【2025-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-07】 + --:-- [直播-关注] <|living_begin|> +【2025-05-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-19】 + --:-- [直播-关注] <|living_begin|> +【2025-06-30】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-22】 + --:-- [直播-关注] <|living_begin|> +【2025-07-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-30】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + 13:12 [广告-深度转化] <|ad_begin|> + 13:38 [广告-深度转化] <|ad_begin|> +【2025-12-09】 + --:-- [直播-打赏] <|living_begin|> + 11:55 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:04 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + 09:32 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:47 [广告-深度转化] <|ad_begin|> + 23:04 [广告-深度转化] <|ad_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> + 22:56 [广告-深度转化] <|ad_begin|> +【2025-12-14】 + 01:47 [广告-深度转化] <|ad_begin|> + 22:46 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> + 22:00 [广告-深度转化] <|ad_begin|> + 23:52 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 00:02 [广告-深度转化] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 22:56 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 23:01 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:17 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + 09:19 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 01:11 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 07:42 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 08:53 [广告-深度转化] <|ad_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 12:12 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 08:53 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 08:27 [广告-深度转化] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 13:37 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 08:09 [广告-深度转化] <|ad_begin|> + 08:09 [广告-点击] <|ad_begin|> + 21:54 [广告-点击] <|ad_begin|> +【2026-01-04】 + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 12:43 [广告-深度转化] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> + 22:56 [广告-深度转化] <|ad_begin|> + 22:56 [广告-点击] <|ad_begin|> + 23:06 [广告-深度转化] <|ad_begin|> + 23:06 [广告-点击] <|ad_begin|> +【2026-01-05】 + 14:09 [广告-点击] <|ad_begin|> +【2026-01-06】 + 11:43 [广告-深度转化] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> +【2026-01-07】 + 10:50 [广告-深度转化] <|ad_begin|> + 10:50 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 23:39 [广告-点击] <|ad_begin|> +【2026-01-09】 + 05:59 [广告-点击] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> + 06:03 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + 13:47 [广告-点击] <|ad_begin|> +【2026-01-10】 + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> +【2026-01-11】 + 00:48 [广告-深度转化] <|ad_begin|> + 00:48 [广告-点击] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2026-01-12】 + 08:00 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:04 [广告-点击] <|ad_begin|> + 08:05 [广告-点击] <|ad_begin|> + 08:09 [广告-点击] <|ad_begin|> +【2026-01-13】 + 22:12 [广告-点击] <|ad_begin|> + 22:14 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:11 [广告-深度转化] <|ad_begin|> + 13:11 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 05:54 [广告-深度转化] <|ad_begin|> + 05:54 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:33 [广告-深度转化] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:12 [广告-深度转化] <|ad_begin|> + 13:12 [广告-点击] <|ad_begin|> + 22:21 [广告-点击] <|ad_begin|> +【2026-01-19】 + 10:02 [广告-深度转化] <|ad_begin|> + 10:02 [广告-点击] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 00:19 [广告-深度转化] <|ad_begin|> + 00:19 [广告-点击] <|ad_begin|> + 00:53 [广告-深度转化] <|ad_begin|> + 00:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + 03:14 [广告-深度转化] <|ad_begin|> + 03:14 [广告-点击] <|ad_begin|> + 04:27 [广告-深度转化] <|ad_begin|> + 04:27 [广告-点击] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 07:47 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 05:51 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:38 [广告-点击] <|ad_begin|> +【2026-01-23】 + 01:36 [广告-点击] <|ad_begin|> + 01:42 [广告-深度转化] <|ad_begin|> + 01:42 [广告-点击] <|ad_begin|> + 04:17 [视频-点赞/长播] <|video_begin|> + 13:28 [视频-点赞/长播] <|video_begin|> + 14:54 [广告-深度转化] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> + 21:11 [广告-深度转化] <|ad_begin|> + 21:11 [广告-点击] <|ad_begin|> + 22:21 [视频-点赞/长播] <|video_begin|> + 22:21 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 09:53 [广告-深度转化] <|ad_begin|> + 09:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:48 [广告-深度转化] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> +【2026-01-25】 + 09:42 [广告-深度转化] <|ad_begin|> + 09:42 [广告-点击] <|ad_begin|> + 10:03 [视频-点赞/长播] <|video_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-26】 + 00:28 [广告-深度转化] <|ad_begin|> + 00:28 [广告-点击] <|ad_begin|> + 05:46 [视频-点赞/长播] <|video_begin|> + 10:23 [广告-点击] <|ad_begin|> + 14:07 [广告-深度转化] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 14:10 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-收藏/长播] <|video_begin|> + 14:49 [视频-点赞/长播] <|video_begin|> + 23:12 [视频-点赞/长播] <|video_begin|> + 23:12 [视频-点赞/长播] <|video_begin|> + 23:17 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 03:18 [视频-点赞/长播] <|video_begin|> + 03:18 [视频-点赞/长播] <|video_begin|> + 03:38 [广告-深度转化] <|ad_begin|> + 03:38 [广告-点击] <|ad_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> +【2026-01-28】 + 00:40 [广告-点击] <|ad_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:26 [视频-点赞/长播] <|video_begin|> + 09:26 [视频-点赞/长播] <|video_begin|> + 09:44 [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-购买] <|prod_begin|> + 14:00 [视频-点赞/长播] <|video_begin|> + 23:04 [广告-深度转化] <|ad_begin|> + 23:04 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:20 [广告-深度转化] <|ad_begin|> + 00:20 [广告-点击] <|ad_begin|> + 06:50 [广告-深度转化] <|ad_begin|> + 06:50 [广告-点击] <|ad_begin|> + 10:54 [视频-点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:18 [视频-长播] <|video_begin|> + 13:18 [视频-长播] <|video_begin|> + 13:18 [视频-点赞/长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-点赞/长播] <|video_begin|> + 13:22 [视频-点赞/长播] <|video_begin|> + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> + 23:07 [视频-点赞/长播] <|video_begin|> + 23:50 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:03 [视频-长播] <|video_begin|> + 00:03 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 22:03 [视频-长播] <|video_begin|> + 22:03 [视频-点赞/长播] <|video_begin|> + 22:04 [视频-长播] <|video_begin|> + 22:30 [视频-长播] <|video_begin|> +【2026-01-31】 + 06:56 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> + 10:34 [视频-点赞/长播] <|video_begin|> + 10:38 [广告-点击] <|ad_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> +【2026-02-01】 + 13:34 [视频-点赞/长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-收藏/长播] <|video_begin|> +【2026-02-02】 + 10:05 [视频-长播] <|video_begin|> + 10:53 [视频-长播] <|video_begin|> + 10:53 [视频-点赞/长播] <|video_begin|> + 10:53 [视频-点赞/长播] <|video_begin|> + 11:05 [视频-长播] <|video_begin|> + 11:08 [视频-长播] <|video_begin|> + 11:08 [视频-长播] <|video_begin|> + 11:13 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:41 [视频-长播] <|video_begin|> + 23:41 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:24 [广告-深度转化] <|ad_begin|> + 00:24 [广告-点击] <|ad_begin|> + 00:53 [广告-点击] <|ad_begin|> + 07:43 [视频-长播] <|video_begin|> + 08:34 [视频-点赞/长播] <|video_begin|> + 08:36 [视频-长播] <|video_begin|> + 08:36 [视频-点赞/长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:56 [广告-深度转化] <|ad_begin|> + 08:56 [广告-点击] <|ad_begin|> + 09:22 [视频-长播] <|video_begin|> + 09:22 [视频-点赞/长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:31 [视频-点赞/长播] <|video_begin|> + 09:31 [视频-点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:46 [视频-长播] <|video_begin|> + 12:53 [视频-长播] <|video_begin|> + 12:53 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 22:13 [视频-点赞/长播] <|video_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> + 22:56 [广告-深度转化] <|ad_begin|> + 22:56 [广告-点击] <|ad_begin|> +【2026-02-04】 + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 22:55 [视频-长播] <|video_begin|> + 22:56 [视频-点赞/长播] <|video_begin|> + 23:30 [广告-深度转化] <|ad_begin|> + 23:30 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:21 [视频-点赞/长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-点赞/长播] <|video_begin|> + 00:37 [视频-点赞/长播] <|video_begin|> + 00:44 [视频-长播] <|video_begin|> + 00:44 [视频-点赞/长播] <|video_begin|> + 00:47 [视频-点赞/长播] <|video_begin|> + 00:50 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:56 [视频-点赞/长播] <|video_begin|> + 13:59 [视频-点赞/长播] <|video_begin|> +角色任务：你需要扮演极端严苛的用户行为数据挖掘专家以及数据格式化专家，基于以上用户交互历史，针对给定主题识别并提取具有递进逻辑和内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 所延伸出的需求，严禁提取任何品类平级、内容重复或仅由天气/促销触发的并列凑单行为。 +1.场景驱动协同：交互 B 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +2.兴趣因果递进：交互 B 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请针对以下主题提取行为逻辑链： +主题：从被动信息吸收到主动利用AI工具的认知跃迁 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动信息吸收到主动利用AI工具的认知跃迁", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我将基于这份用户行为日志，以推荐分析师的视角，对用户的行为逻辑进行推理。 + +我的任务是分析用户从被动接收信息到主动探索并利用AI工具的认知转变过程。我会重点关注行为时间线，梳理出导致这一转变的关键节点和内在逻辑。 + +在分析初期，我注意到该用户是一个信息涉猎非常广泛的人，尤其偏爱知识型和资讯型内容。从2025年上半年开始，他就大量关注财经、时政、健康养生等领域的直播，例如解读A股趋势的直播 <|living_begin|>、分析国际局势的直播 <|living_begin|> 以及各类健康科普直播。这表明他有强烈的通过内容消费来获取信息、辅助决策的习惯。 + +随着时间推移，特别是在2026年1月下旬，我发现用户的行为出现了一个关键的转折点。在2026年1月26日，他密集地消费了大量关于股市、国际政治和经济动态的视频内容。在这些内容中，他不仅点赞，还特别收藏了一个分析股市牛熊转变与投资者心态的视频 <|video_begin|>。收藏这个行为对我来说是一个强烈的信号，它意味着用户不再满足于信息的简单浏览，而是希望对复杂的决策逻辑进行深度思考和复盘。我推测，面对海量且充满不确定性的财经信息，他可能感到了认知上的压力和决策上的痛点，从而萌生了寻找更高效信息处理方法或决策辅助工具的想法。 + +这个推测很快在接下来的行为中得到了印证。仅仅三天后，在2026年1月29日，他首次主动点击了一则关于免费人工智能课程的广告 <|ad_begin|>。这是一个非常重要的行为变化，标志着他开始将注意力从“信息本身”转移到了“处理信息的工具”上。他不再仅仅是被动地接收财经博主的观点，而是开始主动探索能够赋能自己的新技术。这说明他已经意识到，AI可能就是解决他当前信息处理瓶颈的潜在方案。 + +为了进一步确认他对“工具”和“效率”的追求，我继续观察后续行为。在2026年2月1日，他又收藏了一个关于荣耀手机七大实用功能的视频 <|video_begin|>，内容聚焦于如何通过手机自带的功能（如分屏、长截屏）来提升操作效率。这个行为虽然看似与AI没有直接关联，但其内在动机与探索AI工具是高度一致的，都指向了“利用工具提升个人效率”这一核心诉求。这表明他的认知已经发生了转变，从一个纯粹的内容消费者，开始向一个积极寻求并利用工具来优化个人工作流的“效率追求者”演变。 + +综合来看，我认为用户的行为逻辑链条清晰地展现了一个认知跃迁的过程。他最初通过大量观看财经、时政类视频积累信息，在信息过载中遇到了决策瓶颈，这具体体现在他收藏了关于投资策略与心理学研究的视频 <|video_begin|>上。这个痛点驱动他开始寻找解决方案，于是他主动点击了免费人工智能课程的广告 <|ad_begin|>，迈出了探索新工具的第一步。最后，他收藏手机高效操作技巧的视频 <|video_begin|>，进一步巩固了他对于利用工具提升效率的认知和兴趣。因此，我倾向于认为，该用户未来对各类能提升信息处理效率、辅助决策的AI工具或相关课程内容，会有较高的接受度和付费转化可能。 +{"logic_chain": {"name": "从被动信息吸收到主动利用AI工具的认知跃迁", "events": [{"date": "2026-01-26", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "信息积累与痛点形成：用户通过大量观看财经、科技、时政类视频，积累了海量信息，并收藏了关于“投资策略与心理学”的深度分析视频，表明其在信息处理和决策上可能遇到了瓶颈，产生了对更高效工具的需求。"}, {"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣转向与工具探索：用户开始主动点击“免费人工智能课程”广告，标志着其兴趣从被动接收信息转向主动学习和了解能提升信息处理效率的AI工具，行为模式发生显著变化。"}, {"date": "2026-02-01", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "认知深化与效率追求：用户收藏了关于“手机实用功能”以提升操作效率的视频。这一行为与探索AI工具的逻辑一致，共同指向其核心诉求——利用工具提升个人效率，进一步佐证了其从“信息消费”到“工具赋能”的认知转变。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-29】 + --:-- [直播-关注] <|living_begin|> +【2025-02-09】 + --:-- [直播-关注] <|living_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> +【2025-05-05】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-09-03】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [直播-关注] <|living_begin|> +【2025-09-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + 15:56 [广告-点击] <|ad_begin|> + 15:56 [广告-点击] <|ad_begin|> + 15:57 [广告-点击] <|ad_begin|> +【2025-12-10】 + 00:06 [广告-点击] <|ad_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 12:39 [广告-点击] <|ad_begin|> +【2025-12-17】 + 01:22 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + 12:42 [广告-点击] <|ad_begin|> +【2025-12-22】 + 12:11 [广告-点击] <|ad_begin|> + 13:32 [广告-点击] <|ad_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 00:09 [广告-点击] <|ad_begin|> + 00:09 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 18:51 [广告-点击] <|ad_begin|> + 23:08 [广告-点击] <|ad_begin|> +【2025-12-28】 + 01:10 [广告-点击] <|ad_begin|> + 14:16 [广告-点击] <|ad_begin|> + 23:50 [广告-点击] <|ad_begin|> +【2025-12-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + 22:13 [广告-点击] <|ad_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:22 [广告-点击] <|ad_begin|> +【2026-01-10】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-25】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + 05:16 [广告-点击] <|ad_begin|> + 11:15 [视频-转发/长播] <|video_begin|> +【2026-01-27】 + 14:55 [视频-关注/长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + --:-- [商品-购买] <|prod_begin|> +【2026-02-01】 + --:-- [商品-购买] <|prod_begin|> + 12:56 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 09:20 [广告-点击] <|ad_begin|> +【2026-02-03】 + 10:12 [视频-转发/长播] <|video_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-05】 + 01:34 [视频-长播] <|video_begin|> + 01:34 [视频-长播] <|video_begin|> + 01:35 [视频-长播] <|video_begin|> + 01:35 [视频-长播] <|video_begin|> + 01:36 [视频-长播] <|video_begin|> + 01:36 [视频-长播] <|video_begin|> + 03:25 [视频-长播] <|video_begin|> + 03:30 [视频-长播] <|video_begin|> + 03:30 [视频-长播] <|video_begin|> + 04:32 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:06 [视频-长播] <|video_begin|> + 09:06 [视频-长播] <|video_begin|> + 09:08 [视频-长播] <|video_begin|> + 09:09 [视频-长播] <|video_begin|> + 09:10 [视频-长播] <|video_begin|> + 09:21 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:36 [视频-长播] <|video_begin|> + 09:36 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 09:53 [视频-长播] <|video_begin|> + 09:53 [视频-长播] <|video_begin|> + 09:53 [视频-长播] <|video_begin|> + 09:53 [视频-长播] <|video_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 10:00 [视频-长播] <|video_begin|> + 10:01 [视频-长播] <|video_begin|> + 10:01 [视频-长播] <|video_begin|> + 10:07 [视频-长播] <|video_begin|> + 10:10 [视频-长播] <|video_begin|> + 10:17 [视频-长播] <|video_begin|> + 10:17 [视频-长播] <|video_begin|> + 12:14 [视频-长播] <|video_begin|> + 12:14 [视频-长播] <|video_begin|> + 12:14 [视频-长播] <|video_begin|> + 12:18 [视频-长播] <|video_begin|> + 12:19 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 13:46 [视频-长播] <|video_begin|> + 13:46 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你需要扮演极端严苛的用户行为数据挖掘专家以及数据格式化专家，基于以上用户交互历史，针对给定主题识别并提取具有递进逻辑和内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从被动接收到主动决策的购车意向演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收到主动决策的购车意向演化", + "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，我将基于这份用户行为数据，对“从被动接收到主动决策的购车意向演化”这一主题进行推理分析。 + +首先，我注意到这项任务要求我梳理出用户在购车意向上的演变过程，这个过程需要体现出从被动接收信息到主动探索，并最终做出决策的逻辑递进关系。我的分析将严格依据时间线，重点关注那些能够揭示用户心理和行为转变的关键节点。 + +在2025年12月16日之前，我发现用户的行为主要集中在观看美女、才艺类直播以及购买一些食品和生活日用品上，地域标签显示出对青海地区的偏好，但并未显露出任何与汽车相关的明确兴趣。 + +转折点出现在2025年12月16日。我观察到用户在当天点击了一则关于长安神骐T30 SUV的广告<|ad_begin|>。这则广告内容很具体，提到了发动机、质保、置换补贴等信息。我认为这很可能是一个被动的、偶然的兴趣触发点，广告的推送成功地将“购车”这一潜在需求带入了用户的视野，让他开始初步认知长安这个品牌及其优惠政策。 + +紧接着，就在同一天，用户的行为发生了显著的变化。他不再仅仅是被动地看广告，而是主动关注了一个讲解二手车的直播间<|living_begin|>。这个直播间的主播风格成熟干练，内容聚焦于车辆的全面讲解、车况剖析和价格沟通。从被动点击广告到主动关注专业讲解直播，我推断用户的兴趣已经被激发，他开始寻求更深入、更全面的信息，希望从一个可信赖的渠道了解车辆的细节，这标志着他的认知状态从简单的“信息接收”升级到了主动的“知识获取”。 + +在接下来的大约两周时间里，用户的行为轨迹持续强化了这一购车意向。他点击了多条汽车相关的广告，涉及长安、荣威、捷途等多个品牌，内容涵盖了促销、置换补贴和车型展示，例如长安第二代X5 PLUS的促销广告<|ad_begin|>和年底清仓卡车销售信息<|ad_begin|>。同时，他开始购买一些汽车周边用品，比如车载香水<|prod_begin|>、汽车摆件<|prod_begin|>和电动车灯饰<|prod_begin|>。这些购买行为虽然还比较泛化，但已经表明他正在为拥有车辆后的生活场景做一些初步的想象和准备。 + +最终，在2025年12月31日，我看到了一个决定性的行为。用户购买了一件针对性极强的商品：长安星卡plus中控台避光垫<|prod_begin|>。这个行为的意义非同寻常。购买一款特定车型的专用配件，强烈地暗示出用户的购车决策已经基本完成，并且目标已经精准地收敛到了“长安星卡plus”这一具体车型上。这不再是泛泛的兴趣，而是为即将拥有的车辆添置物品的实际行动。这一购买行为，标志着他已经从线上信息收集阶段，转化到了为线下拥有车辆做实体准备的阶段，形成了一个从意向萌发到决策行动的完整闭环。 + +综合以上分析，我认为用户的购车意向演化路径清晰地展现了一个从被动到主动、从宽泛到聚焦的决策过程。整个逻辑链条始于一次偶然的广告点击，随后通过主动关注专业直播深化认知，最终通过购买特定车型的专用配件，确认了其购车决策。因此，我倾向于认为，用户最初因点击长安汽车广告<|ad_begin|>而产生兴趣，接着通过关注二手车讲解直播<|living_begin|>来深入了解信息，最终通过购买长安星卡plus的避光垫<|prod_begin|>这一行为，明确了他的购车目标。 +{"logic_chain": {"name": "从被动接收到主动决策的购车意向演化", "events": [{"date": "2025-12-16", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收到长安品牌汽车广告，初步建立对该品牌的认知，兴趣点集中在性能和优惠政策上。"}, {"date": "2025-12-16", "action": "[直播-关注] <|living_begin|>", "logic": "认知深化：从被动看广告转向主动关注专业讲解直播，寻求更深入、全面的车辆信息（如车况细节、价格），体现了从“信息接收”到“知识获取”的认知升级。"}, {"date": "2025-12-31", "action": "[商品-购买] <|prod_begin|>", "logic": "决策收敛与行动闭环：在了解汽车信息后，用户购买了针对“长安星卡plus”这一具体车型的内饰配件。这一行为不仅确认了用户的购车决策已收敛至特定车型，也标志着从线上信息收集到线下实体准备的转化，形成了完整的意向到行动的闭环。"}]}}<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, 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<|prod_begin|>、<|prod_begin|>。这表明他/她是一个典型的二次元和游戏爱好者，对喜爱的IP有很强的付费意愿和收藏偏好。同时，用户的消费行为也展现出对低价、高性价比商品（如各种9.9元商品）和可爱、二次元风格小物件的偏爱。 + +在2026年1月26日之前，用户的视频消费内容非常多样化，涵盖了萌宠、搞笑、社会生活、动漫解说等多个领域，但没有明显集中于某一特定IP。直到1月27日，我观察到一个显著的兴趣转折点。当天，用户首次点击了一则关于《明日方舟》新版本“终末地”的宣传广告 <|ad_begin|>。这次点击似乎是一个关键的触发点，为后续的行为埋下了伏笔。 + +这次广告点击之后，用户的行为立刻显示出对这个新信息的强烈反应。仅仅几个小时后，我就看到他/她开始主动消费与“终末地”相关的深度内容，比如收藏并长时间观看了分析管理员角色背景和世界观设定的视频 <|video_begin|>。这种从被动接收广告信息到主动探索深度内容的转变，并且伴随着收藏和点赞这类高价值互动，清晰地表明用户的兴趣被成功激发，并且正在快速加深。 + +接下来的几天，这种兴趣的深化趋势愈发明显。我发现用户的关注点从“这是个什么故事”迅速升级为“这个游戏该怎么玩”。在1月29日，他/她收藏并观看了介绍“终末地”卡池规则和抽卡策略的视频 <|video_begin|>。这说明用户已经产生了强烈的游玩意愿，开始为进入游戏做准备，研究核心的付费机制和资源分配策略。这种行为已经超越了普通内容消费者的范畴，表现出准玩家的特征。 + +仅仅两天后，在1月31日，用户的探索行为变得更加具体和硬核。他/她开始观看关于游戏内“基建布局”和“材料制作”这类高阶攻略视频 <|video_begin|>。这表明用户的认知已经从“想玩”进化到了“想玩好”，正在预习具体的游戏技巧，希望在游戏开始时就能高效推进。这种对复杂游戏系统的深入研究，标志着其沉浸程度已经达到了一个新高度。 + +最终，在2月5日，我观察到这条兴趣链形成了一个完美的闭环。用户的兴趣从纯粹的线上数字内容，延伸到了与现实生活相关的实体商品。他/她点击了《明日方舟：终末地》的联名交通卡 <|prod_begin|>。点击购买游戏IP的实体周边，尤其是交通卡这种具有身份标识和日常使用属性的物品，强烈地表明了用户对该IP的高度认同感。这不仅是消费行为，更是一种身份的宣告。 + +综合来看，我认为用户的行为清晰地展现了一个从被动接收信息到深度沉浸于特定游戏IP的完整过程。这个过程始于一次广告点击 <|ad_begin|>，随后通过观看角色背景解析视频 <|video_begin|> 来深化兴趣，接着通过研究卡池规则 <|video_begin|> 和基建攻略 <|video_begin|> 为实际游玩做准备，最终通过点击联名实体周边 <|prod_begin|> 完成了从线上兴趣到线下身份认同的转化。基于此，我推测该用户未来有很大概率会成为《明日方舟：终末地》的活跃玩家，并可能继续关注和消费该IP的各类内容与商品。 +{"logic_chain": {"name": "从泛化娱乐到特定游戏IP的深度沉浸", "events": [{"date": "2026-01-27", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：通过广告被动接收到《明日方舟》新版本“终末地”的核心信息，这是兴趣链的起点。"}, {"date": "2026-01-27", "action": "[视频-收藏/点赞/长播] <|video_begin|>", "logic": "兴趣深化与验证：从广告的泛化信息，转向对特定角色背景和世界观设定的深度内容消费，交互从“点击”升级为“收藏/点赞/长播”，表明兴趣被初步确认并加深。"}, {"date": "2026-01-29", "action": "[视频-转发/收藏/点赞/长播] <|video_begin|>", "logic": "认知跃迁与行为准备：兴趣从“了解故事”演变为“研究玩法”，开始关注“卡池规则”、“保底数值”等核心游戏机制和攻略型信息，体现出从旁观者向潜在玩家转化的明确意图。"}, {"date": "2026-01-31", "action": "[视频-收藏/点赞/长播] <|video_begin|>", "logic": "策略收敛与技能预习：进一步聚焦于更具体的“基建布局”、“材料制作”等高阶游戏攻略，表明用户不仅想玩，而且希望高效地玩好，认知深度已达到资深玩家水平。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "兴趣闭环与身份认同：用户的兴趣从线上内容消费延伸至线下实体周边，点击联名交通卡这一行为，标志着其对该游戏IP的认同感已经形成，完成了从“了解”到“认同”的完整认知闭环。"}]}}<|im_end|> + +好的，我将基于这份用户行为日志，以推荐分析师的视角，对用户的行为逻辑进行推理。 + +我的任务是分析用户从被动接收信息到主动探索并利用AI工具的认知转变过程。我会重点关注行为时间线，梳理出导致这一转变的关键节点和内在逻辑。 + +在分析初期，我注意到该用户是一个信息涉猎非常广泛的人，尤其偏爱知识型和资讯型内容。从2025年上半年开始，他就大量关注财经、时政、健康养生等领域的直播，例如解读A股趋势的直播 <|living_begin|>、分析国际局势的直播 <|living_begin|> 以及各类健康科普直播。这表明他有强烈的通过内容消费来获取信息、辅助决策的习惯。 + +随着时间推移，特别是在2026年1月下旬，我发现用户的行为出现了一个关键的转折点。在2026年1月26日，他密集地消费了大量关于股市、国际政治和经济动态的视频内容。在这些内容中，他不仅点赞，还特别收藏了一个分析股市牛熊转变与投资者心态的视频 <|video_begin|>。收藏这个行为对我来说是一个强烈的信号，它意味着用户不再满足于信息的简单浏览，而是希望对复杂的决策逻辑进行深度思考和复盘。我推测，面对海量且充满不确定性的财经信息，他可能感到了认知上的压力和决策上的痛点，从而萌生了寻找更高效信息处理方法或决策辅助工具的想法。 + +这个推测很快在接下来的行为中得到了印证。仅仅三天后，在2026年1月29日，他首次主动点击了一则关于免费人工智能课程的广告 <|ad_begin|>。这是一个非常重要的行为变化，标志着他开始将注意力从“信息本身”转移到了“处理信息的工具”上。他不再仅仅是被动地接收财经博主的观点，而是开始主动探索能够赋能自己的新技术。这说明他已经意识到，AI可能就是解决他当前信息处理瓶颈的潜在方案。 + +为了进一步确认他对“工具”和“效率”的追求，我继续观察后续行为。在2026年2月1日，他又收藏了一个关于荣耀手机七大实用功能的视频 <|video_begin|>，内容聚焦于如何通过手机自带的功能（如分屏、长截屏）来提升操作效率。这个行为虽然看似与AI没有直接关联，但其内在动机与探索AI工具是高度一致的，都指向了“利用工具提升个人效率”这一核心诉求。这表明他的认知已经发生了转变，从一个纯粹的内容消费者，开始向一个积极寻求并利用工具来优化个人工作流的“效率追求者”演变。 + +综合来看，我认为用户的行为逻辑链条清晰地展现了一个认知跃迁的过程。他最初通过大量观看财经、时政类视频积累信息，在信息过载中遇到了决策瓶颈，这具体体现在他收藏了关于投资策略与心理学研究的视频 <|video_begin|>上。这个痛点驱动他开始寻找解决方案，于是他主动点击了免费人工智能课程的广告 <|ad_begin|>，迈出了探索新工具的第一步。最后，他收藏手机高效操作技巧的视频 <|video_begin|>，进一步巩固了他对于利用工具提升效率的认知和兴趣。因此，我倾向于认为，该用户未来对各类能提升信息处理效率、辅助决策的AI工具或相关课程内容，会有较高的接受度和付费转化可能。 +{"logic_chain": {"name": "从被动信息吸收到主动利用AI工具的认知跃迁", "events": [{"date": "2026-01-26", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "信息积累与痛点形成：用户通过大量观看财经、科技、时政类视频，积累了海量信息，并收藏了关于“投资策略与心理学”的深度分析视频，表明其在信息处理和决策上可能遇到了瓶颈，产生了对更高效工具的需求。"}, {"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣转向与工具探索：用户开始主动点击“免费人工智能课程”广告，标志着其兴趣从被动接收信息转向主动学习和了解能提升信息处理效率的AI工具，行为模式发生显著变化。"}, {"date": "2026-02-01", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "认知深化与效率追求：用户收藏了关于“手机实用功能”以提升操作效率的视频。这一行为与探索AI工具的逻辑一致，共同指向其核心诉求——利用工具提升个人效率，进一步佐证了其从“信息消费”到“工具赋能”的认知转变。"}]}}<|im_end|> + +好的，我将基于这份用户行为数据，对“从被动接收到主动决策的购车意向演化”这一主题进行推理分析。 + +首先，我注意到这项任务要求我梳理出用户在购车意向上的演变过程，这个过程需要体现出从被动接收信息到主动探索，并最终做出决策的逻辑递进关系。我的分析将严格依据时间线，重点关注那些能够揭示用户心理和行为转变的关键节点。 + +在2025年12月16日之前，我发现用户的行为主要集中在观看美女、才艺类直播以及购买一些食品和生活日用品上，地域标签显示出对青海地区的偏好，但并未显露出任何与汽车相关的明确兴趣。 + +转折点出现在2025年12月16日。我观察到用户在当天点击了一则关于长安神骐T30 SUV的广告<|ad_begin|>。这则广告内容很具体，提到了发动机、质保、置换补贴等信息。我认为这很可能是一个被动的、偶然的兴趣触发点，广告的推送成功地将“购车”这一潜在需求带入了用户的视野，让他开始初步认知长安这个品牌及其优惠政策。 + +紧接着，就在同一天，用户的行为发生了显著的变化。他不再仅仅是被动地看广告，而是主动关注了一个讲解二手车的直播间<|living_begin|>。这个直播间的主播风格成熟干练，内容聚焦于车辆的全面讲解、车况剖析和价格沟通。从被动点击广告到主动关注专业讲解直播，我推断用户的兴趣已经被激发，他开始寻求更深入、更全面的信息，希望从一个可信赖的渠道了解车辆的细节，这标志着他的认知状态从简单的“信息接收”升级到了主动的“知识获取”。 + +在接下来的大约两周时间里，用户的行为轨迹持续强化了这一购车意向。他点击了多条汽车相关的广告，涉及长安、荣威、捷途等多个品牌，内容涵盖了促销、置换补贴和车型展示，例如长安第二代X5 PLUS的促销广告<|ad_begin|>和年底清仓卡车销售信息<|ad_begin|>。同时，他开始购买一些汽车周边用品，比如车载香水<|prod_begin|>、汽车摆件<|prod_begin|>和电动车灯饰<|prod_begin|>。这些购买行为虽然还比较泛化，但已经表明他正在为拥有车辆后的生活场景做一些初步的想象和准备。 + +最终，在2025年12月31日，我看到了一个决定性的行为。用户购买了一件针对性极强的商品：长安星卡plus中控台避光垫<|prod_begin|>。这个行为的意义非同寻常。购买一款特定车型的专用配件，强烈地暗示出用户的购车决策已经基本完成，并且目标已经精准地收敛到了“长安星卡plus”这一具体车型上。这不再是泛泛的兴趣，而是为即将拥有的车辆添置物品的实际行动。这一购买行为，标志着他已经从线上信息收集阶段，转化到了为线下拥有车辆做实体准备的阶段，形成了一个从意向萌发到决策行动的完整闭环。 + +综合以上分析，我认为用户的购车意向演化路径清晰地展现了一个从被动到主动、从宽泛到聚焦的决策过程。整个逻辑链条始于一次偶然的广告点击，随后通过主动关注专业直播深化认知，最终通过购买特定车型的专用配件，确认了其购车决策。因此，我倾向于认为，用户最初因点击长安汽车广告<|ad_begin|>而产生兴趣，接着通过关注二手车讲解直播<|living_begin|>来深入了解信息，最终通过购买长安星卡plus的避光垫<|prod_begin|>这一行为，明确了他的购车目标。 +{"logic_chain": {"name": "从被动接收到主动决策的购车意向演化", "events": [{"date": "2025-12-16", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收到长安品牌汽车广告，初步建立对该品牌的认知，兴趣点集中在性能和优惠政策上。"}, {"date": "2025-12-16", "action": "[直播-关注] <|living_begin|>", "logic": "认知深化：从被动看广告转向主动关注专业讲解直播，寻求更深入、全面的车辆信息（如车况细节、价格），体现了从“信息接收”到“知识获取”的认知升级。"}, {"date": "2025-12-31", "action": "[商品-购买] <|prod_begin|>", "logic": "决策收敛与行动闭环：在了解汽车信息后，用户购买了针对“长安星卡plus”这一具体车型的内饰配件。这一行为不仅确认了用户的购车决策已收敛至特定车型，也标志着从线上信息收集到线下实体准备的转化，形成了完整的意向到行动的闭环。"}]}}<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-06 04:54:33,443 >> 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 04:54:33,444 >> 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 04:54:33] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-06 04:54:33] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-06 04:54:34,174 >> 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 04:54:34,174 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-06 04:54:34,227 >> 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 04:54:35,016 >> 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 04:54:35,346 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-06 04:54:35] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-06 04:54:35] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-06 04:54:35] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-06 04:54:35] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-06 04:54:35] 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 04:54:35,370 >> 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 04:54:35,486 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-06 04:54:35,486 >> Num examples = 2,451 +[INFO|trainer.py:1471] 2026-07-06 04:54:35,486 >> Num Epochs = 2 +[INFO|trainer.py:1472] 2026-07-06 04:54:35,486 >> Num update steps per epoch = 613 +[INFO|trainer.py:1473] 2026-07-06 04:54:35,486 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-06 04:54:35,486 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-06 04:54:35,486 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-06 04:54:35,486 >> Total optimization steps = 1,226 +[INFO|trainer.py:1479] 2026-07-06 04:54:35,487 >> Number of trainable parameters = 801,433,600 + 0%| | 0/1226 [00:00> `use_return_dict` is deprecated! 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'grad_norm': '3.625', 'learning_rate': '6.9e-06', 'epoch': '0.7915'} +{'loss': '1.485', 'grad_norm': '2.094', 'learning_rate': '6.838e-06', 'epoch': '0.7997'} +{'loss': '1.496', 'grad_norm': '3.156', 'learning_rate': '6.777e-06', 'epoch': '0.8078'} +{'loss': '1.407', 'grad_norm': '3.812', 'learning_rate': '6.715e-06', 'epoch': '0.816'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-500/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 06:07:44,057 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-500/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 06:07:44,080 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-500/tokenizer_config.json + 41%|████ | 501/1226 [1:13:19<2:00:51, 10.00s/it] 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/home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1000 +[INFO|configuration_utils.py:533] 2026-07-06 07:20:49,064 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1000/config.json +[INFO|configuration_utils.py:816] 2026-07-06 07:20:49,064 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1000/generation_config.json +{'loss': '1.395', 'grad_norm': '2.484', 'learning_rate': '5.231e-06', 'epoch': '1.003'} +{'loss': '1.514', 'grad_norm': '4.156', 'learning_rate': '5.165e-06', 'epoch': '1.011'} +{'loss': '1.523', 'grad_norm': '2.688', 'learning_rate': '5.099e-06', 'epoch': '1.02'} +{'loss': '1.449', 'grad_norm': '2.781', 'learning_rate': '5.033e-06', 'epoch': '1.028'} +{'loss': '1.507', 'grad_norm': '2.562', 'learning_rate': '4.967e-06', 'epoch': '1.036'} +{'loss': '1.467', 'grad_norm': '2.969', 'learning_rate': '4.901e-06', 'epoch': '1.044'} +{'loss': '1.602', 'grad_norm': '3.938', 'learning_rate': '4.835e-06', 'epoch': '1.052'} +{'loss': '1.454', 'grad_norm': '3.141', 'learning_rate': '4.769e-06', 'epoch': '1.06'} +{'loss': '1.494', 'grad_norm': '2.672', 'learning_rate': '4.703e-06', 'epoch': '1.069'} +{'loss': '1.437', 'grad_norm': '3.656', 'learning_rate': '4.637e-06', 'epoch': '1.077'} +{'loss': '1.496', 'grad_norm': '3.719', 'learning_rate': '4.571e-06', 'epoch': '1.085'} +{'loss': '1.402', 'grad_norm': '3.453', 'learning_rate': '4.505e-06', 'epoch': '1.093'} +{'loss': '1.567', 'grad_norm': '3.453', 'learning_rate': '4.44e-06', 'epoch': '1.101'} +{'loss': '1.416', 'grad_norm': '4.031', 'learning_rate': '4.374e-06', 'epoch': '1.109'} +{'loss': '1.503', 'grad_norm': '2.453', 'learning_rate': '4.309e-06', 'epoch': '1.118'} +{'loss': '1.466', 'grad_norm': '4.438', 'learning_rate': '4.243e-06', 'epoch': '1.126'} +{'loss': '1.446', 'grad_norm': '4.25', 'learning_rate': 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'grad_norm': '3.141', 'learning_rate': '2.74e-06', 'epoch': '1.322'} +{'loss': '1.56', 'grad_norm': '2.922', 'learning_rate': '2.681e-06', 'epoch': '1.33'} +{'loss': '1.407', 'grad_norm': '4.188', 'learning_rate': '2.623e-06', 'epoch': '1.338'} +{'loss': '1.453', 'grad_norm': '4.031', 'learning_rate': '2.565e-06', 'epoch': '1.346'} +{'loss': '1.447', 'grad_norm': '2.734', 'learning_rate': '2.508e-06', 'epoch': '1.354'} +{'loss': '1.43', 'grad_norm': '2.531', 'learning_rate': '2.451e-06', 'epoch': '1.362'} +{'loss': '1.466', 'grad_norm': '3.047', 'learning_rate': '2.394e-06', 'epoch': '1.37'} +{'loss': '1.413', 'grad_norm': '4.312', 'learning_rate': '2.338e-06', 'epoch': '1.379'} +{'loss': '1.415', 'grad_norm': '2.734', 'learning_rate': '2.282e-06', 'epoch': '1.387'} +{'loss': '1.502', 'grad_norm': '4.188', 'learning_rate': '2.227e-06', 'epoch': '1.395'} +{'loss': '1.52', 'grad_norm': '2.766', 'learning_rate': '2.172e-06', 'epoch': '1.403'} +{'loss': '1.364', 'grad_norm': '4.188', 'learning_rate': '2.118e-06', 'epoch': '1.411'} +{'loss': '1.407', 'grad_norm': '4.125', 'learning_rate': '2.064e-06', 'epoch': '1.419'} +{'loss': '1.423', 'grad_norm': '2.953', 'learning_rate': '2.011e-06', 'epoch': '1.428'} +{'loss': '1.479', 'grad_norm': '3.109', 'learning_rate': '1.958e-06', 'epoch': '1.436'} +{'loss': '1.471', 'grad_norm': '3.078', 'learning_rate': '1.906e-06', 'epoch': '1.444'} +{'loss': '1.459', 'grad_norm': '3.062', 'learning_rate': '1.855e-06', 'epoch': '1.452'} +{'loss': '1.34', 'grad_norm': '3.109', 'learning_rate': '1.804e-06', 'epoch': '1.46'} +{'loss': '1.38', 'grad_norm': '3.109', 'learning_rate': '1.753e-06', 'epoch': '1.468'} +{'loss': '1.461', 'grad_norm': '2.781', 'learning_rate': '1.703e-06', 'epoch': '1.477'} +{'loss': '1.436', 'grad_norm': '4', 'learning_rate': '1.654e-06', 'epoch': '1.485'} +{'loss': '1.465', 'grad_norm': '3.797', 'learning_rate': '1.605e-06', 'epoch': '1.493'} +{'loss': '1.431', 'grad_norm': '2.375', 'learning_rate': '1.557e-06', 'epoch': '1.501'} +{'loss': '1.448', 'grad_norm': '2.609', 'learning_rate': '1.509e-06', 'epoch': '1.509'} +{'loss': '1.459', 'grad_norm': '3.047', 'learning_rate': '1.462e-06', 'epoch': '1.517'} +{'loss': '1.448', 'grad_norm': '3.812', 'learning_rate': '1.416e-06', 'epoch': '1.525'} +{'loss': '1.472', 'grad_norm': '3.75', 'learning_rate': '1.37e-06', 'epoch': '1.534'} +{'loss': '1.443', 'grad_norm': '3.828', 'learning_rate': '1.325e-06', 'epoch': '1.542'} +{'loss': '1.44', 'grad_norm': '2.453', 'learning_rate': '1.28e-06', 'epoch': '1.55'} +{'loss': '1.384', 'grad_norm': '3.656', 'learning_rate': '1.237e-06', 'epoch': '1.558'} +{'loss': '1.438', 'grad_norm': '4.656', 'learning_rate': '1.193e-06', 'epoch': '1.566'} +{'loss': '1.446', 'grad_norm': '4.219', 'learning_rate': '1.151e-06', 'epoch': '1.574'} +{'loss': '1.55', 'grad_norm': '2.781', 'learning_rate': '1.109e-06', 'epoch': '1.583'} +{'loss': '1.54', 'grad_norm': '2.422', 'learning_rate': '1.068e-06', 'epoch': '1.591'} +{'loss': '1.394', 'grad_norm': '4.5', 'learning_rate': '1.028e-06', 'epoch': '1.599'} +{'loss': '1.405', 'grad_norm': '2.938', 'learning_rate': '9.878e-07', 'epoch': '1.607'} +{'loss': '1.598', 'grad_norm': '2.5', 'learning_rate': '9.487e-07', 'epoch': '1.615'} +{'loss': '1.503', 'grad_norm': '4.062', 'learning_rate': '9.104e-07', 'epoch': '1.623'} +{'loss': '1.394', 'grad_norm': '4.344', 'learning_rate': '8.727e-07', 'epoch': '1.632'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1000/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 07:20:50,085 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1000/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 07:20:50,108 >> tokenizer config file saved in 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'learning_rate': '8.358e-07', 'epoch': '1.64'} +{'loss': '1.5', 'grad_norm': '2.906', 'learning_rate': '7.996e-07', 'epoch': '1.648'} +{'loss': '1.494', 'grad_norm': '2.484', 'learning_rate': '7.641e-07', 'epoch': '1.656'} +{'loss': '1.535', 'grad_norm': '4.031', 'learning_rate': '7.294e-07', 'epoch': '1.664'} +{'loss': '1.393', 'grad_norm': '3.281', 'learning_rate': '6.954e-07', 'epoch': '1.672'} +{'loss': '1.441', 'grad_norm': '3.453', 'learning_rate': '6.622e-07', 'epoch': '1.681'} +{'loss': '1.55', 'grad_norm': '2.328', 'learning_rate': '6.297e-07', 'epoch': '1.689'} +{'loss': '1.479', 'grad_norm': '2.5', 'learning_rate': '5.98e-07', 'epoch': '1.697'} +{'loss': '1.529', 'grad_norm': '3.141', 'learning_rate': '5.671e-07', 'epoch': '1.705'} +{'loss': '1.459', 'grad_norm': '3.312', 'learning_rate': '5.369e-07', 'epoch': '1.713'} +{'loss': '1.452', 'grad_norm': '2.922', 'learning_rate': '5.075e-07', 'epoch': '1.721'} +{'loss': '1.539', 'grad_norm': '2.359', 'learning_rate': '4.789e-07', 'epoch': '1.729'} +{'loss': '1.492', 'grad_norm': '2.797', 'learning_rate': '4.511e-07', 'epoch': '1.738'} +{'loss': '1.475', 'grad_norm': '3.25', 'learning_rate': '4.241e-07', 'epoch': '1.746'} +{'loss': '1.391', 'grad_norm': '3.172', 'learning_rate': '3.978e-07', 'epoch': '1.754'} +{'loss': '1.504', 'grad_norm': '2.469', 'learning_rate': '3.724e-07', 'epoch': '1.762'} +{'loss': '1.393', 'grad_norm': '3.625', 'learning_rate': '3.478e-07', 'epoch': '1.77'} +{'loss': '1.384', 'grad_norm': '3.594', 'learning_rate': '3.24e-07', 'epoch': '1.778'} +{'loss': '1.541', 'grad_norm': '2.094', 'learning_rate': '3.01e-07', 'epoch': '1.787'} +{'loss': '1.456', 'grad_norm': '3.188', 'learning_rate': '2.789e-07', 'epoch': '1.795'} +{'loss': '1.497', 'grad_norm': '2.156', 'learning_rate': '2.575e-07', 'epoch': '1.803'} +{'loss': '1.383', 'grad_norm': '4.188', 'learning_rate': '2.37e-07', 'epoch': '1.811'} +{'loss': '1.535', 'grad_norm': '3.172', 'learning_rate': '2.173e-07', 'epoch': '1.819'} +{'loss': '1.414', 'grad_norm': '3.125', 'learning_rate': '1.985e-07', 'epoch': '1.827'} +{'loss': '1.472', 'grad_norm': '2.859', 'learning_rate': '1.805e-07', 'epoch': '1.836'} +{'loss': '1.304', 'grad_norm': '4.438', 'learning_rate': '1.633e-07', 'epoch': '1.844'} +{'loss': '1.413', 'grad_norm': '4.094', 'learning_rate': '1.47e-07', 'epoch': '1.852'} +{'loss': '1.397', 'grad_norm': '2.172', 'learning_rate': '1.315e-07', 'epoch': '1.86'} +{'loss': '1.529', 'grad_norm': '2.5', 'learning_rate': '1.169e-07', 'epoch': '1.868'} +{'loss': '1.404', 'grad_norm': '2.859', 'learning_rate': '1.031e-07', 'epoch': '1.876'} +{'loss': '1.401', 'grad_norm': '3.781', 'learning_rate': '9.021e-08', 'epoch': '1.885'} +{'loss': '1.494', 'grad_norm': '3.234', 'learning_rate': '7.814e-08', 'epoch': '1.893'} +{'loss': '1.487', 'grad_norm': '2.234', 'learning_rate': '6.694e-08', 'epoch': '1.901'} +{'loss': '1.389', 'grad_norm': '4.25', 'learning_rate': '5.66e-08', 'epoch': '1.909'} +{'loss': '1.4', 'grad_norm': '2.984', 'learning_rate': '4.712e-08', 'epoch': '1.917'} +{'loss': '1.402', 'grad_norm': '3.891', 'learning_rate': '3.85e-08', 'epoch': '1.925'} +{'loss': '1.501', 'grad_norm': '2.938', 'learning_rate': '3.076e-08', 'epoch': '1.933'} +{'loss': '1.405', 'grad_norm': '2.078', 'learning_rate': '2.387e-08', 'epoch': '1.942'} +{'loss': '1.44', 'grad_norm': '3.656', 'learning_rate': '1.786e-08', 'epoch': '1.95'} +{'loss': '1.569', 'grad_norm': '2.391', 'learning_rate': '1.272e-08', 'epoch': '1.958'} +{'loss': '1.404', 'grad_norm': '2.75', 'learning_rate': '8.445e-09', 'epoch': '1.966'} +{'loss': '1.417', 'grad_norm': '3.375', 'learning_rate': '5.043e-09', 'epoch': '1.974'} +{'loss': '1.461', 'grad_norm': '2.562', 'learning_rate': '2.513e-09', 'epoch': '1.982'} +{'loss': '1.514', 'grad_norm': '2.578', 'learning_rate': '8.552e-10', 'epoch': '1.991'} +{'loss': '1.344', 'grad_norm': '3.547', 'learning_rate': '6.981e-11', 'epoch': '1.999'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1226/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 07:53:43,210 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1226/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 07:53:43,230 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/checkpoint-1226/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-06 07:53:45,253 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 1226/1226 [2:59:09<00:00, 8.38s/it] 100%|██████████| 1226/1226 [2:59:09<00:00, 8.77s/it] +[INFO|trainer.py:3815] 2026-07-06 07:53:45,254 >> Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2 +[INFO|configuration_utils.py:533] 2026-07-06 07:53:45,255 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/config.json +[INFO|configuration_utils.py:816] 2026-07-06 07:53:45,255 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/generation_config.json +{'train_runtime': '1.075e+04', 'train_samples_per_second': '0.456', 'train_steps_per_second': '0.114', 'train_loss': '1.535', 'epoch': '2'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 07:53:46,083 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 07:53:46,104 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/tokenizer_config.json +***** train metrics ***** + epoch = 2.0 + total_flos = 551649809GF + train_loss = 1.535 + train_runtime = 2:59:09.76 + train_samples_per_second = 0.456 + train_steps_per_second = 0.114 +Figure saved at: /home/ll/llm4rec/experiments/20260706_overnight/outputs/v10_balanced_core_lr1e5_ep2/training_loss.png +[WARNING|2026-07-06 07:53:46] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-06 07:53:46] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-06 07:53:46,211 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} diff --git a/train_results.json b/train_results.json new file mode 100644 index 0000000..776b49e --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 2.0, + "total_flos": 5.923294725916508e+17, + "train_loss": 1.5350287483722507, + "train_runtime": 10749.7665, + "train_samples_per_second": 0.456, + "train_steps_per_second": 0.114 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..2643f45 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,246 @@ +{"current_steps": 5, "total_steps": 1226, "loss": 2.7381839752197266, "lr": 1.0810810810810812e-06, "epoch": 0.008159934720522236, "percentage": 0.41, "elapsed_time": "0:00:44", "remaining_time": "3:02:08"} +{"current_steps": 10, "total_steps": 1226, "loss": 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