From 07ab8ea5e296a14be325e1935f0a46f96c36f759 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 19 Jul 2026 05:08:10 +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/v17_item_caption_cot_lr13e6 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 + ...49451.ubuntu-System-Product-Name.3905398.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2202 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 85 + trainer_state.json | 631 +++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 39245 bytes 16 files changed, 3220 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul08_02-37-31_ubuntu-System-Product-Name/events.out.tfevents.1783449451.ubuntu-System-Product-Name.3905398.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz 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diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..7eeabfd --- /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: v17_item_caption_cot_lr13e6 + results: [] +--- + + + +# v17_item_caption_cot_lr13e6 + +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 exp_item_caption_cot 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: 1.3e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607173 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 1 + +### 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..c328d81 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 2.0218118019963494e+17, + "train_loss": 1.6924350921143876, + "train_runtime": 3361.2318, + "train_samples_per_second": 0.502, + "train_steps_per_second": 0.126 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/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..c1227fa --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa3aae4d2c25bdbbb6600452553b9d069270d0d76733acc50d83c073cf16c116 +size 1602902832 diff --git a/runs/Jul08_02-37-31_ubuntu-System-Product-Name/events.out.tfevents.1783449451.ubuntu-System-Product-Name.3905398.0 b/runs/Jul08_02-37-31_ubuntu-System-Product-Name/events.out.tfevents.1783449451.ubuntu-System-Product-Name.3905398.0 new file mode 100644 index 0000000..95c5077 --- /dev/null +++ b/runs/Jul08_02-37-31_ubuntu-System-Product-Name/events.out.tfevents.1783449451.ubuntu-System-Product-Name.3905398.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d804af73d9aad5d8c36ca91f847cce44c7d73052dd5f96ea109d4870ef2bdcd3 +size 23915 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..9b55322 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": 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..feba324 --- /dev/null +++ b/train.log @@ -0,0 +1,2202 @@ +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-08 02:36:57] 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] 2026-07-08 02:36:58,524 >> 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] 2026-07-08 02:36:58,526 >> 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] 2026-07-08 02:37:02,913 >> 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] 2026-07-08 02:37:02,914 >> 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] 2026-07-08 02:37:03,365 >> 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] 2026-07-08 02:37:03,365 >> 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|2026-07-08 02:37:05] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/item_caption_cot.jsonl... +Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. + Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 16683 examples [00:00, 154939.39 examples/s] Generating train split: 35145 examples [00:00, 159473.27 examples/s] Generating train split: 48461 examples [00:00, 162398.70 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/48461 [00:00user +【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】： +【2025-02-24】 + --:-- [直播-关注] <|living_begin|> +【2025-03-01】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> +【2025-03-21】 + --:-- [直播-关注] <|living_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-05】 + --:-- [直播-关注] <|living_begin|> +【2025-10-06】 + --:-- [直播-关注] <|living_begin|> +【2025-10-07】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [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】 + 00:08 [广告-深度转化] <|ad_begin|> + 02:49 [广告-深度转化] <|ad_begin|> + 03:36 [广告-深度转化] <|ad_begin|> + 03:58 [广告-深度转化] <|ad_begin|> + 11:17 [广告-深度转化] <|ad_begin|> +【2025-12-14】 + 04:48 [广告-深度转化] <|ad_begin|> + 11:36 [广告-深度转化] <|ad_begin|> + 13:12 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + 03:45 [广告-深度转化] <|ad_begin|> + 05:53 [广告-深度转化] <|ad_begin|> + 10:50 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 11:36 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 04:07 [广告-深度转化] <|ad_begin|> + 04:47 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 12:12 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + 12:26 [广告-深度转化] <|ad_begin|> + 12:54 [广告-深度转化] <|ad_begin|> + 13:35 [广告-深度转化] <|ad_begin|> + 14:03 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 00:12 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 13:13 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + 00:47 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + 23:43 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-16】 + 11:59 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 05:59 [广告-深度转化] <|ad_begin|> + 12:22 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + 09:39 [广告-深度转化] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-23】 + 08:57 [视频-评论/长播] <|video_begin|> + 09:35 [视频-转发/长播] <|video_begin|> + 09:45 [视频-转发/收藏/点赞/长播] <|video_begin|> + 12:05 [视频-转发/长播] <|video_begin|> + 12:27 [广告-深度转化] <|ad_begin|> + 13:14 [广告-深度转化] <|ad_begin|> + 13:35 [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|> +【2026-01-24】 + 00:29 [视频-转发/长播] <|video_begin|> + 00:40 [视频-转发/长播] <|video_begin|> + 00:40 [视频-转发/长播] <|video_begin|> + 00:40 [视频-转发/收藏/点赞/长播] <|video_begin|> + 00:48 [视频-转发/长播] <|video_begin|> + 02:35 [视频-转发/长播] <|video_begin|> + 02:35 [视频-转发/点赞/长播] <|video_begin|> + 02:47 [视频-转发/点赞/长播] <|video_begin|> + 02:47 [视频-收藏/长播] <|video_begin|> + 02:52 [视频-转发/长播] <|video_begin|> + 02:52 [视频-点赞/长播] <|video_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 10:37 [视频-关注/长播] <|video_begin|> + 12:06 [广告-深度转化] <|ad_begin|> + 12:25 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 23:53 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 02:31 [广告-深度转化] <|ad_begin|> + 07:14 [视频-转发/长播] <|video_begin|> + 07:24 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 08:11 [视频-转发/长播] <|video_begin|> + 08:20 [视频-转发/长播] <|video_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 11:03 [视频-评论/长播] <|video_begin|> +【2026-01-27】 + 07:21 [视频-转发/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 01:11 [视频-转发/长播] <|video_begin|> + 02:03 [视频-转发/长播] <|video_begin|> + 02:32 [视频-转发/长播] <|video_begin|> + 02:42 [视频-点赞/长播] <|video_begin|> + 03:11 [视频-转发/长播] <|video_begin|> + 03:18 [视频-关注/点赞/长播] <|video_begin|> + 04:35 [视频-关注/转发/评论/收藏/点赞/长播] <|video_begin|> + 06:44 [视频-转发/长播] <|video_begin|> + 06:44 [视频-点赞/长播] <|video_begin|> + 06:44 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 06:44 [视频-关注/收藏/点赞/长播] <|video_begin|> + 06:57 [广告-点击] <|ad_begin|> + 07:53 [广告-点击] <|ad_begin|> + 10:59 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 12:20 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 12:24 [视频-点赞/长播] <|video_begin|> + 12:40 [视频-收藏/长播] <|video_begin|> + 12:45 [视频-收藏/点赞/长播] <|video_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 23:30 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 23:35 [视频-关注/收藏/点赞/长播] <|video_begin|> +【2026-01-29】 + 00:29 [视频-转发/长播] <|video_begin|> + 00:47 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 00:47 [广告-深度转化] <|ad_begin|> + 00:47 [广告-点击] <|ad_begin|> + 03:13 [广告-深度转化] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 03:54 [广告-点击] <|ad_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:45 [广告-点击] <|ad_begin|> + 05:10 [视频-点赞/长播] <|video_begin|> + 06:21 [广告-深度转化] <|ad_begin|> + 06:21 [广告-点击] <|ad_begin|> + 07:01 [广告-深度转化] <|ad_begin|> + 07:01 [广告-点击] <|ad_begin|> + 07:05 [广告-深度转化] <|ad_begin|> + 07:05 [广告-点击] <|ad_begin|> + 08:00 [视频-转发/长播] <|video_begin|> + 08:00 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 08:06 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 08:07 [广告-深度转化] <|ad_begin|> + 08:07 [广告-点击] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> + 08:24 [广告-点击] <|ad_begin|> + 09:03 [广告-点击] <|ad_begin|> + 09:16 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> + 09:25 [广告-点击] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> + 12:02 [广告-深度转化] <|ad_begin|> + 12:02 [广告-点击] <|ad_begin|> + 12:08 [视频-转发/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-30】 + 06:05 [视频-转发/长播] <|video_begin|> + 11:11 [广告-点击] <|ad_begin|> + 11:14 [广告-点击] <|ad_begin|> + 13:14 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 13:26 [广告-点击] <|ad_begin|> + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-31】 + 00:57 [视频-收藏/长播] <|video_begin|> + 03:41 [视频-转发/长播] <|video_begin|> + 03:46 [广告-点击] <|ad_begin|> + 03:56 [视频-转发/长播] <|video_begin|> + 04:04 [广告-深度转化] <|ad_begin|> + 04:04 [广告-点击] <|ad_begin|> + 06:43 [视频-转发/长播] <|video_begin|> + 06:58 [广告-点击] <|ad_begin|> + 07:44 [广告-点击] <|ad_begin|> + 08:01 [广告-点击] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> + 09:39 [广告-点击] <|ad_begin|> + 10:02 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:18 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + 00:17 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 00:20 [广告-深度转化] <|ad_begin|> + 00:20 [广告-点击] <|ad_begin|> + 00:27 [视频-转发/长播] <|video_begin|> + 00:55 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 00:57 [广告-点击] <|ad_begin|> + 10:47 [广告-深度转化] <|ad_begin|> + 10:47 [广告-点击] <|ad_begin|> + 12:01 [广告-点击] <|ad_begin|> + 12:04 [视频-转发/长播] <|video_begin|> + 14:18 [视频-浏览] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-02】 + 03:20 [广告-点击] <|ad_begin|> + 04:06 [视频-浏览] <|video_begin|> + 06:41 [视频-转发/长播] <|video_begin|> + 06:56 [视频-转发/长播] <|video_begin|> + 08:29 [广告-点击] <|ad_begin|> + 10:46 [广告-深度转化] <|ad_begin|> + 10:46 [广告-点击] <|ad_begin|> + 11:02 [广告-深度转化] <|ad_begin|> + 11:02 [广告-点击] <|ad_begin|> + 11:14 [广告-点击] <|ad_begin|> + 11:15 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:22 [广告-点击] <|ad_begin|> + 11:26 [广告-深度转化] <|ad_begin|> + 11:26 [广告-点击] <|ad_begin|> + 12:00 [广告-深度转化] <|ad_begin|> + 12:00 [广告-点击] <|ad_begin|> + 12:06 [广告-深度转化] <|ad_begin|> + 12:06 [广告-点击] <|ad_begin|> + 12:38 [广告-深度转化] <|ad_begin|> + 12:38 [广告-点击] <|ad_begin|> + 12:53 [广告-深度转化] <|ad_begin|> + 12:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-03】 + 00:04 [广告-点击] <|ad_begin|> + 00:26 [视频-关注/收藏/点赞/长播] <|video_begin|> + 01:11 [视频-评论/长播] <|video_begin|> + 02:44 [广告-深度转化] <|ad_begin|> + 02:44 [广告-点击] <|ad_begin|> + 03:08 [视频-点赞/长播] <|video_begin|> + 03:17 [视频-评论/长播] <|video_begin|> + 04:29 [视频-转发/长播] <|video_begin|> + 08:40 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 08:51 [视频-转发/长播] <|video_begin|> + 09:39 [广告-点击] <|ad_begin|> + 09:39 [广告-点击] <|ad_begin|> + 10:36 [广告-深度转化] <|ad_begin|> + 10:36 [广告-点击] <|ad_begin|> + 11:20 [视频-转发/长播] <|video_begin|> + 13:13 [广告-深度转化] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:34 [视频-转发/长播] <|video_begin|> + 13:46 [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-购买] <|prod_begin|> +【2026-02-04】 + 00:14 [视频-评论/长播] <|video_begin|> + 01:29 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 01:38 [广告-点击] <|ad_begin|> + 03:46 [广告-点击] <|ad_begin|> + 04:24 [视频-关注/长播] <|video_begin|> + 04:33 [广告-深度转化] <|ad_begin|> + 04:33 [广告-点击] <|ad_begin|> + 06:00 [视频-点赞/长播] <|video_begin|> + 09:36 [视频-评论/长播] <|video_begin|> + 09:52 [视频-转发/长播] <|video_begin|> + 09:52 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + 10:18 [广告-点击] <|ad_begin|> + 10:28 [广告-深度转化] <|ad_begin|> + 10:28 [广告-点击] <|ad_begin|> + 12:41 [广告-点击] <|ad_begin|> + 12:45 [广告-深度转化] <|ad_begin|> + 12:45 [广告-点击] <|ad_begin|> + 13:56 [视频-关注/转发/收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-05】 + 00:35 [视频-长播] <|video_begin|> + 00:38 [视频-长播] <|video_begin|> + 00:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-评论/长播] <|video_begin|> + 06:17 [视频-评论/长播] <|video_begin|> + 06:28 [视频-长播] <|video_begin|> + 06:28 [视频-评论/长播] <|video_begin|> + 06:28 [视频-关注/收藏/长播] <|video_begin|> + 06:40 [视频-长播] <|video_begin|> + 06:40 [视频-长播] <|video_begin|> + 06:40 [视频-长播] <|video_begin|> + 06:56 [视频-长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:03 [视频-长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:07 [视频-评论/长播] <|video_begin|> + 07:17 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:21 [视频-长播] <|video_begin|> + 07:28 [视频-长播] <|video_begin|> + 07:28 [视频-长播] <|video_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 08:37 [视频-长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:52 [视频-长播] <|video_begin|> + 08:52 [视频-长播] <|video_begin|> + 08:58 [广告-深度转化] <|ad_begin|> + 08:58 [广告-点击] <|ad_begin|> + 10:06 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:06 [广告-深度转化] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-关注/收藏/点赞/长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:08 [视频-长播] <|video_begin|> + 14:08 [视频-长播] <|video_begin|> + 14:14 [广告-深度转化] <|ad_begin|> + 14:14 [广告-点击] <|ad_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-收藏/长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，依据以上用户交互历史，围绕目标主题，精确提取出所有相关的历史交互信息。 + +主题：从被动娱乐到主动探索：对《我的世界》游戏机制的深度探究 + +输出格式要求：请仅输出包含 SID 的 JSON 数组，严禁输出任何额外的解释、说明或 Markdown 标记。/no_think<|im_end|> +<|im_start|>assistant + + +["<|ad_begin|>", "<|video_begin|>", "<|prod_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + 14:20 [广告-深度转化] <|ad_begin|> + 19:59 [广告-深度转化] <|ad_begin|> + 20:26 [广告-深度转化] <|ad_begin|> + 22:00 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:40 [广告-深度转化] <|ad_begin|> + 22:27 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + 03:54 [广告-深度转化] <|ad_begin|> + 12:40 [广告-深度转化] <|ad_begin|> + 20:40 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + 09:35 [广告-深度转化] <|ad_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 17:33 [广告-深度转化] <|ad_begin|> + 20:42 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> + 22:34 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 22:26 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 01:48 [广告-深度转化] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 07:43 [广告-深度转化] <|ad_begin|> + 12:23 [广告-深度转化] <|ad_begin|> + 21:59 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 03:36 [广告-深度转化] <|ad_begin|> + 03:36 [广告-点击] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 23:00 [广告-点击] <|ad_begin|> + 23:53 [广告-深度转化] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2025-12-30】 + 01:52 [广告-深度转化] <|ad_begin|> + 01:52 [广告-点击] <|ad_begin|> + 03:56 [广告-深度转化] <|ad_begin|> + 03:56 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> +【2025-12-31】 + 01:21 [广告-深度转化] <|ad_begin|> + 01:21 [广告-点击] <|ad_begin|> + 19:55 [广告-深度转化] <|ad_begin|> + 19:55 [广告-点击] <|ad_begin|> +【2026-01-01】 + 00:15 [广告-深度转化] <|ad_begin|> + 00:15 [广告-点击] <|ad_begin|> + 22:19 [广告-深度转化] <|ad_begin|> + 22:19 [广告-点击] <|ad_begin|> + 22:59 [广告-深度转化] <|ad_begin|> + 22:59 [广告-点击] <|ad_begin|> +【2026-01-02】 + 00:43 [广告-深度转化] <|ad_begin|> + 00:43 [广告-点击] <|ad_begin|> + 03:59 [广告-深度转化] <|ad_begin|> + 03:59 [广告-点击] <|ad_begin|> + 09:08 [广告-点击] <|ad_begin|> +【2026-01-03】 + 09:00 [广告-深度转化] <|ad_begin|> + 09:00 [广告-点击] <|ad_begin|> +【2026-01-04】 + 09:23 [广告-深度转化] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> + 12:12 [广告-点击] <|ad_begin|> +【2026-01-05】 + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + 00:54 [广告-深度转化] <|ad_begin|> + 00:54 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + 14:01 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 11:52 [广告-深度转化] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 12:07 [广告-点击] <|ad_begin|> + 12:18 [广告-点击] <|ad_begin|> +【2026-01-10】 + 17:51 [广告-深度转化] <|ad_begin|> + 17:51 [广告-点击] <|ad_begin|> + 18:44 [广告-深度转化] <|ad_begin|> + 18:44 [广告-点击] <|ad_begin|> +【2026-01-11】 + 20:01 [广告-深度转化] <|ad_begin|> + 20:01 [广告-点击] <|ad_begin|> +【2026-01-12】 + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 08:27 [广告-深度转化] <|ad_begin|> + 08:27 [广告-点击] <|ad_begin|> +【2026-01-13】 + 02:20 [广告-深度转化] <|ad_begin|> + 02:20 [广告-点击] <|ad_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:16 [广告-点击] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> +【2026-01-14】 + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> +【2026-01-15】 + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-点击] <|ad_begin|> + 08:21 [广告-点击] <|ad_begin|> +【2026-01-16】 + 00:43 [广告-深度转化] <|ad_begin|> + 00:43 [广告-点击] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 06:52 [广告-深度转化] <|ad_begin|> + 06:52 [广告-点击] <|ad_begin|> + 07:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [商品-购买] <|prod_begin|> + 20:11 [视频-收藏/长播] <|video_begin|> + 20:12 [视频-收藏/长播] <|video_begin|> + 20:24 [视频-收藏/长播] <|video_begin|> + 20:40 [视频-收藏/长播] <|video_begin|> + 20:42 [视频-收藏/长播] <|video_begin|> +【2026-01-22】 + 01:54 [视频-收藏/长播] <|video_begin|> + 04:47 [视频-收藏/长播] <|video_begin|> + 04:47 [视频-收藏/长播] <|video_begin|> + 04:51 [视频-收藏/长播] <|video_begin|> + 07:14 [视频-收藏/长播] <|video_begin|> + 07:37 [视频-收藏/长播] <|video_begin|> + 22:29 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + 05:07 [视频-收藏/长播] <|video_begin|> + 08:21 [视频-点赞/长播] <|video_begin|> + 08:40 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:17 [广告-点击] <|ad_begin|> + 20:57 [视频-收藏/长播] <|video_begin|> +【2026-01-24】 + 00:28 [视频-收藏/长播] <|video_begin|> + 01:36 [视频-收藏/长播] <|video_begin|> + 02:36 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 05:06 [视频-收藏/长播] <|video_begin|> + 05:08 [视频-收藏/长播] <|video_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 05:52 [视频-收藏/长播] <|video_begin|> + 08:03 [视频-收藏/长播] <|video_begin|> + 14:20 [视频-收藏/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|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> +【2026-01-25】 + 13:26 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:12 [广告-深度转化] <|ad_begin|> + 20:12 [广告-点击] <|ad_begin|> +【2026-01-26】 + 19:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> +【2026-01-27】 + 00:11 [视频-收藏/长播] <|video_begin|> + 02:59 [视频-评论/长播] <|video_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 06:39 [广告-深度转化] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 11:41 [视频-点赞/长播] <|video_begin|> + 12:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:13 [广告-深度转化] <|ad_begin|> + 22:13 [广告-点击] <|ad_begin|> +【2026-01-28】 + 03:30 [广告-深度转化] <|ad_begin|> + 03:30 [广告-点击] <|ad_begin|> + 05:23 [广告-深度转化] <|ad_begin|> + 05:23 [广告-点击] <|ad_begin|> + 06:18 [视频-点赞/长播] <|video_begin|> + 06:23 [视频-点赞/长播] <|video_begin|> + 07:46 [视频-收藏/长播] <|video_begin|> + 07:48 [视频-收藏/长播] <|video_begin|> + 08:00 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:32 [视频-收藏/长播] <|video_begin|> +【2026-01-29】 + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 05:10 [视频-收藏/长播] <|video_begin|> + 06:19 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:57 [视频-收藏/长播] <|video_begin|> +【2026-01-30】 + 01:30 [广告-深度转化] <|ad_begin|> + 01:30 [广告-点击] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:38 [广告-点击] <|ad_begin|> + 20:52 [广告-深度转化] <|ad_begin|> + 20:52 [广告-点击] <|ad_begin|> +【2026-01-31】 + 01:34 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + 00:02 [广告-深度转化] <|ad_begin|> + 00:02 [广告-点击] <|ad_begin|> + 06:07 [视频-浏览] <|video_begin|> + 06:29 [广告-深度转化] <|ad_begin|> + 06:29 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:21 [广告-点击] <|ad_begin|> + 23:43 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-02】 + 00:47 [广告-点击] <|ad_begin|> + 10:49 [广告-深度转化] <|ad_begin|> + 10:49 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:25 [广告-深度转化] <|ad_begin|> + 23:25 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:36 [广告-点击] <|ad_begin|> + 01:04 [视频-收藏/长播] <|video_begin|> + 02:13 [视频-评论/长播] <|video_begin|> + 02:39 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 03:56 [视频-收藏/长播] <|video_begin|> + 04:04 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [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|> + 21:00 [广告-深度转化] <|ad_begin|> + 21:00 [广告-点击] <|ad_begin|> +【2026-02-04】 + 02:05 [广告-深度转化] <|ad_begin|> + 02:05 [广告-点击] <|ad_begin|> + 08:00 [广告-深度转化] <|ad_begin|> + 08:00 [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|> + 19:40 [视频-点赞/长播] <|video_begin|> + 20:16 [视频-收藏/长播] <|video_begin|> +【2026-02-05】 + 00:02 [视频-收藏/长播] <|video_begin|> + 00:02 [视频-收藏/长播] <|video_begin|> + 06:55 [视频-长播] <|video_begin|> + 06:58 [视频-长播] <|video_begin|> + 06:58 [视频-收藏/点赞/长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 07:02 [视频-长播] <|video_begin|> + 07:02 [视频-长播] <|video_begin|> + 07:02 [视频-关注/长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:13 [视频-长播] <|video_begin|> + 07:18 [视频-长播] <|video_begin|> + 07:23 [视频-长播] <|video_begin|> + 07:25 [视频-长播] <|video_begin|> + 07:28 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:40 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:45 [视频-长播] <|video_begin|> + 07:51 [视频-长播] <|video_begin|> + 07:53 [视频-收藏/长播] <|video_begin|> + 07:55 [视频-长播] <|video_begin|> + 07:56 [视频-长播] <|video_begin|> + 08:00 [视频-长播] <|video_begin|> + 08:00 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:22 [视频-长播] <|video_begin|> + 12:22 [视频-长播] <|video_begin|> + 12:24 [视频-长播] <|video_begin|> + 12:24 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +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 必须在同一生活场景、主题类目下具有极强的需求补完关系。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从健康焦虑到精准化饮食管理的认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从健康焦虑到精准化饮食管理的认知演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这项任务。我的目标是根据用户的交互历史，推理出一条关于用户从产生健康焦虑到寻求精准饮食管理方案的行为演变路径。 + +我首先注意到，用户的行为在2026年2月之前，主要集中在居家生活用品、服饰美妆以及大量的情感、家庭、悬疑类短剧广告的消费上，这描绘出一个关注家庭生活和娱乐内容的女性形象。然而，从2月1日开始，我观察到了一个明显的兴趣拐点。 + +在2月1日，用户浏览了一个关于甲状腺术后康复过程的视频 <|video_begin|>。这个视频内容非常具体且带有一定的医疗严肃性，与她之前偏向轻松娱乐的内容消费习惯有显著差异。我推测，这个行为可能源于用户自身或身边亲友的健康状况变化，触发了她对特定健康问题的关注和焦虑。这不仅仅是泛泛的养生兴趣，而是由一个具体问题驱动的、带有明确指向性的信息搜集行为。 + +紧接着，在2月3日，用户的关注点从术后即时状态，深化到了长期的恢复与生活管理。她观看并评论了一个分享甲状腺手术四年后通过健康生活方式成功减重并保持积极心态的视频 <|video_begin|>。这个行为的递进性非常清晰：从了解“发生了什么”（术后监护），到探索“我该怎么做”（长期康复经验）。用户通过观看他人的成功案例，不仅在寻求心理上的共鸣和鼓励，更是在寻找一种可供自己参考和实践的、综合性的生活方式解决方案，这表明她的需求已经从单纯的信息获取升级为寻求可行的行动指南。 + +两天后的2月5日，用户的探索行为变得更加聚焦和专业化。她观看了一个由医生讲解的视频 <|video_begin|>，内容是关于富含特定营养素（硒）的水果对甲状腺健康的作用。这是一个关键的认知收敛过程。相比于前两天较为宽泛的“健康生活方式”，这次她开始关注具体的营养元素和精准的食疗方案。这说明用户已经不满足于笼统的建议，而是希望获得更具科学依据、更具象化的指导，比如“应该具体吃什么”。这个行为完成了从“问题认知”到“综合性方案探索”，再到“精准化对策学习”的完整认知闭环。 + +综合来看，我认为用户的行为展现了一个清晰的演化路径。她可能因为某个触发事件（浏览甲状腺术后视频 <|video_begin|>）产生了健康焦虑，随后通过观看他人长期的康复经验分享 <|video_begin|> 来寻求综合性的生活方式指导，最终将需求收敛到寻求医生讲解的、针对特定营养素的精准食疗方案 <|video_begin|>。这一系列行为表明，她对健康管理的认知正在从模糊走向清晰，从被动接收信息转向主动寻求专业的、可执行的解决方案，未来可能会对相关的健康食品、营养补充剂或更专业的医疗健康内容表现出较高的兴趣。 +{"logic_chain": {"name": "从健康焦虑到精准化饮食管理的认知演化", "events": [{"date": "2026-02-01", "action": "[视频-浏览] <|video_begin|>", "logic": "问题触发：通过浏览甲状腺术后康复视频，表现出对该健康问题的初步关注。"}, {"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣深化：从关注术后即时状态转向长期康复和生活方式管理，通过观看他人经验分享，寻求可行的实践路径。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知收敛与增量：从泛化的生活方式管理，聚焦到具体的营养素（硒）和食疗方案，完成了从“问题是什么”到“具体怎么做”的认知闭环，获得了明确的行动指导。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2023-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-05】 + 08:32 [广告-深度转化] <|ad_begin|> + 11:58 [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|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + 02:46 [广告-深度转化] <|ad_begin|> + 04:49 [广告-深度转化] <|ad_begin|> + 07:40 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-07】 + 07:48 [广告-深度转化] <|ad_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 13:51 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 01:19 [广告-深度转化] <|ad_begin|> + 06:53 [广告-深度转化] <|ad_begin|> + 10:04 [广告-深度转化] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 14:31 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 02:15 [广告-深度转化] <|ad_begin|> + 07:05 [广告-深度转化] <|ad_begin|> + 13:03 [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|> +【2026-01-10】 + 06:46 [广告-深度转化] <|ad_begin|> + 13:38 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-11】 + 01:11 [广告-深度转化] <|ad_begin|> + 07:31 [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|> +【2026-01-12】 + 03:46 [广告-深度转化] <|ad_begin|> + 04:34 [广告-深度转化] <|ad_begin|> + 07:02 [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|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + 08:22 [广告-深度转化] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> + 14:57 [广告-深度转化] <|ad_begin|> + 14:57 [广告-点击] <|ad_begin|> +【2026-01-14】 + 06:23 [广告-深度转化] <|ad_begin|> + 06:23 [广告-点击] <|ad_begin|> + 07:20 [广告-深度转化] <|ad_begin|> + 07:20 [广告-点击] <|ad_begin|> + 13:08 [广告-深度转化] <|ad_begin|> + 13:08 [广告-点击] <|ad_begin|> +【2026-01-15】 + 00:47 [广告-点击] <|ad_begin|> + 05:30 [广告-深度转化] <|ad_begin|> + 05:30 [广告-点击] <|ad_begin|> + 12:38 [广告-点击] <|ad_begin|> + 14:39 [广告-深度转化] <|ad_begin|> + 14:39 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + 04:36 [广告-深度转化] <|ad_begin|> + 04:36 [广告-点击] <|ad_begin|> + 12:28 [广告-深度转化] <|ad_begin|> + 12:28 [广告-点击] <|ad_begin|> + 14:03 [广告-深度转化] <|ad_begin|> + 14:03 [广告-点击] <|ad_begin|> + 15:16 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:31 [广告-点击] <|ad_begin|> +【2026-01-17】 + 00:09 [广告-点击] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 13:03 [广告-深度转化] <|ad_begin|> + 13:03 [广告-点击] <|ad_begin|> +【2026-01-18】 + 04:21 [广告-深度转化] <|ad_begin|> + 04:21 [广告-点击] <|ad_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + 15:02 [广告-深度转化] <|ad_begin|> + 15:02 [广告-点击] <|ad_begin|> +【2026-01-19】 + 04:43 [广告-点击] <|ad_begin|> + 08:54 [广告-深度转化] <|ad_begin|> + 08:54 [广告-点击] <|ad_begin|> + 14:25 [广告-深度转化] <|ad_begin|> + 14:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 09:38 [广告-深度转化] <|ad_begin|> + 09:38 [广告-点击] <|ad_begin|> +【2026-01-21】 + 02:48 [广告-深度转化] <|ad_begin|> + 02:48 [广告-点击] <|ad_begin|> + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 08:37 [广告-深度转化] <|ad_begin|> + 08:37 [广告-点击] <|ad_begin|> + 12:17 [广告-深度转化] <|ad_begin|> + 12:17 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-23】 + 09:40 [广告-点击] <|ad_begin|> + 10:00 [广告-深度转化] <|ad_begin|> + 10:00 [广告-点击] <|ad_begin|> + 15:17 [广告-深度转化] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> +【2026-01-24】 + 09:32 [广告-深度转化] <|ad_begin|> + 09:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-25】 + 05:27 [视频-点赞/长播] <|video_begin|> + 12:56 [广告-点击] <|ad_begin|> +【2026-01-26】 + 05:42 [广告-深度转化] <|ad_begin|> + 05:42 [广告-点击] <|ad_begin|> + 05:43 [广告-点击] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 10:17 [视频-点赞/长播] <|video_begin|> + 12:43 [广告-点击] <|ad_begin|> +【2026-01-27】 + 03:49 [广告-深度转化] <|ad_begin|> + 03:49 [广告-点击] <|ad_begin|> + 14:26 [广告-深度转化] <|ad_begin|> + 14:26 [广告-点击] <|ad_begin|> + 14:33 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 18:35 [广告-点击] <|ad_begin|> + 18:54 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 01:06 [广告-深度转化] <|ad_begin|> + 01:06 [广告-点击] <|ad_begin|> + 01:06 [广告-点击] <|ad_begin|> + 04:25 [广告-深度转化] <|ad_begin|> + 04:25 [广告-点击] <|ad_begin|> + 06:36 [广告-深度转化] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> + 07:27 [广告-深度转化] <|ad_begin|> + 07:27 [广告-点击] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 14:30 [广告-深度转化] <|ad_begin|> + 14:30 [广告-点击] <|ad_begin|> +【2026-01-29】 + 04:10 [广告-深度转化] <|ad_begin|> + 04:10 [广告-点击] <|ad_begin|> + 05:00 [广告-深度转化] <|ad_begin|> + 05:00 [广告-点击] <|ad_begin|> + 09:59 [广告-点击] <|ad_begin|> + 11:44 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:59 [广告-深度转化] <|ad_begin|> + 12:59 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 06:34 [广告-深度转化] <|ad_begin|> + 06:34 [广告-点击] <|ad_begin|> + 13:41 [广告-点击] <|ad_begin|> +【2026-01-31】 + 00:44 [广告-点击] <|ad_begin|> + 03:01 [广告-点击] <|ad_begin|> + 03:27 [广告-点击] <|ad_begin|> + 14:59 [广告-深度转化] <|ad_begin|> + 14:59 [广告-点击] <|ad_begin|> +【2026-02-01】 + 00:02 [视频-关注/长播] <|video_begin|> + 01:03 [广告-深度转化] <|ad_begin|> + 01:03 [广告-点击] <|ad_begin|> + 08:11 [视频-长播] <|video_begin|> + 08:11 [视频-长播] <|video_begin|> + 08:11 [视频-长播] <|video_begin|> + 08:39 [视频-长播] <|video_begin|> +【2026-02-02】 + 00:35 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 03:17 [视频-长播] <|video_begin|> + 03:19 [视频-长播] <|video_begin|> + 03:19 [视频-长播] <|video_begin|> + 03:29 [视频-长播] <|video_begin|> + 03:29 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 13:19 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> + 15:33 [视频-长播] <|video_begin|> + 15:33 [视频-长播] <|video_begin|> + 15:33 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> + 22:31 [视频-长播] <|video_begin|> +【2026-02-03】 + 02:37 [视频-长播] <|video_begin|> + 10:35 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 15:12 [视频-长播] <|video_begin|> + 15:31 [视频-长播] <|video_begin|> + 15:31 [视频-长播] <|video_begin|> + 15:39 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:49 [视频-长播] <|video_begin|> + 15:49 [视频-长播] <|video_begin|> + 23:03 [视频-长播] <|video_begin|> +【2026-02-04】 + 12:51 [广告-深度转化] <|ad_begin|> + 12:51 [广告-点击] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 23:23 [视频-长播] <|video_begin|> + 23:23 [视频-长播] <|video_begin|> +【2026-02-05】 + 03:40 [视频-点赞/长播] <|video_begin|> + 03:48 [广告-点击] <|ad_begin|> + 04:36 [广告-深度转化] <|ad_begin|> + 04:36 [广告-点击] <|ad_begin|> + 09:58 [视频-长播] <|video_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> + 14:49 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> +角色任务：你是一个极端严苛的用户行为数据挖掘专家以及数据格式化专家，请根据以上用户交互历史，针对给定的主题，提取出具有行为递进逻辑和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须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。 +请针对下列主题提取行为逻辑链： +主题：从被动接受情感剧到主动寻求人生哲理解答的认知跃迁 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接受情感剧到主动寻求人生哲理解答的认知跃迁", + "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，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接手的任务是分析一位用户从被动接受情感短剧内容，到主动寻求人生哲理类书籍的行为转变。我的分析将主要依据其行为时间线，深入挖掘其内在动机和兴趣的演变过程。 + +首先，我注意到在2026年1月初，这位用户的行为模式发生了显著变化。在此之前，他的消费记录非常多样化，涵盖了从户外刀具<|prod_begin|>、食品<|prod_begin|>、日用品<|prod_begin|>到健康产品<|prod_begin|>等多个领域，展现出一种务实、注重生活品质的男性形象。然而，从1月5日开始，他的广告交互记录中密集地出现了大量情感纠葛、家庭伦理、婚姻危机主题的短剧广告。例如，他深度转化了关于离婚后家庭矛盾的《我们曾经有过家》<|ad_begin|>，以及后续多部类似主题的短剧，如《入狱后我执掌乾坤》<|ad_begin|>和《入赘五年，离婚即无敌》<|ad_begin|>。我推测，这种高频次、高强度的内容推送，让他沉浸在了一个充满戏剧性冲突和现实困境的虚拟环境中。这些短剧普遍探讨现代人在爱情、婚姻和自我价值实现中的挣扎，很可能触动了用户在现实生活中的某些思考或困惑。 + +紧接着，一个关键的转折点出现在了2026年1月9日。在连续几天被动接收了大量虚构的情感冲突故事后，我发现他的行为从“看”转向了“学”。他主动点击并购买了名为《渔樵问对》的书籍<|prod_begin|>，这本书的描述明确指向“解决人生困惑”和“感受国学经典智慧”。我认为这并非偶然。在被大量情感剧所营造的复杂问题包围后，用户可能不再满足于仅仅观看别人的故事，而是开始寻求一种能够指导自己、解决内心困惑的系统性知识。这标志着一次重要的认知跃迁：从被动地消费娱乐内容，转向主动地寻求智慧和答案。 + +这种寻求并未停止。在初步接触了宏观的人生哲理之后，用户的探索开始向更具体的领域深化。1月12日，他购买了另一本书籍《道家与性文化》<|prod_begin|>。在我看来，这是对他之前所接触的“情感纠葛”主题的一种更深层次的回应和探索。如果说《渔樵问对》是对普适性人生问题的思考，那么《道家与性文化》则将这种思考聚焦到了两性关系和个人身心修养这一更具体的层面。这表明他的兴趣正在从宽泛的哲学思考，收敛到与个人生活、特别是两性关系紧密相关的特定知识领域。 + +综合来看，我认为用户的行为轨迹清晰地展示了一个从问题沉浸到寻求解答，再到深化探索的完整闭环。他最初被动观看大量以离婚、家庭矛盾为主题的短剧广告，如《我们曾经有过家》<|ad_begin|>，这让他对复杂的情感关系问题产生了认知。随后，这种认知驱动他不再满足于观看故事，而是主动购买了旨在“解决人生困惑”的《渔樵问对》<|prod_begin|>，试图从哲学层面寻找答案。最后，他的探索进一步聚焦，通过购买《道家与性文化》<|prod_begin|>来深入研究与两性关系和个人修养相关的具体知识。因此，我倾向于认为，未来向他推荐兼具深度思考和实用性的国学、哲学或心理学内容，有较大概率被他所接纳。 +{"logic_chain": {"name": "从被动接受情感剧到主动寻求人生哲理解答的认知跃迁", "events": [{"date": "2026-01-06", "action": "[广告-深度转化] <|ad_begin|>", "logic": "问题域沉浸（触发阶段）：用户开始高密度地被动观看以“离婚”、“家庭矛盾”、“情感纠葛”为核心主题的短剧广告，构建了一个充满现实困境与情感挑战的认知背景。"}, {"date": "2026-01-09", "action": "[商品-购买] <|prod_begin|>", "logic": "行为模式转变与认知升级：在连续接触大量虚构的情感冲突后，用户行为从被动观看转为主动购买。购买的书籍《渔樵问对》明确标榜“解决人生困惑”，表明用户不再满足于观看表层故事，而是开始寻求更深层次、更具指导性的哲学智慧来解答现实或心理上的困惑，实现了从“看故事”到“找答案”的认知跃迁。"}, {"date": "2026-01-12", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣深化与领域聚焦：在寻求宏观人生哲理（《渔樵问对》）之后，用户的探索进一步具体化，转向了更细分的“道家与性文化”领域。这表明用户在初步的哲学思考后，开始关注与两性关系、个人养生相关的具体理论，是对前期“情感纠葛”主题的深度回应和探索，体现了从普适性智慧到特定领域知识的兴趣收敛。"}]}}<|im_end|> +<|im_start|>user +你是一位电商商品理解专家，擅长将商品token转换为清晰、完整的商品文字描述。 +告诉我本商品token<|prod_begin|>对应的商品描述/think<|im_end|> +<|im_start|>assistant +面向婴幼儿的寝居用品，选用安全亲肤材质，兼具保暖透气防水等实用功能，设计童趣丰富，详细标注规格与卖点，且统一提供包邮服务。 +这款儿童床垫专为幼儿园及家庭宝宝设计，适用于午睡或日常睡眠，支持冬夏两用，四季皆宜。产品采用柔软舒适材质，表面为明亮黄色，边缘缝线规整，视觉上温馨活泼，适合儿童使用。标签显示其执行标准为GB/T 22796-2021，安全类别为GB 18401-2010A类，环保等级达E1级，保障健康安全。尺寸多样，提供50cm×130cm至88cm×168cm等多种规格，可适配不同儿童床。支持常规水洗，最高洗涤温度30℃，不可漂白、不可熨烫、不可悬挂晾干，维护便捷。作为自营商品，价格为58.2元，免运费，适合批发或家庭批量采购，是幼儿园、早教中心及家庭宝宝午休的理想选择。<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, -100, -100, -100, 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+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|> + + +["<|ad_begin|>", "<|video_begin|>", "<|prod_begin|>", "<|video_begin|>"]<|im_end|> + +好的，我来分析一下这项任务。我的目标是根据用户的交互历史，推理出一条关于用户从产生健康焦虑到寻求精准饮食管理方案的行为演变路径。 + +我首先注意到，用户的行为在2026年2月之前，主要集中在居家生活用品、服饰美妆以及大量的情感、家庭、悬疑类短剧广告的消费上，这描绘出一个关注家庭生活和娱乐内容的女性形象。然而，从2月1日开始，我观察到了一个明显的兴趣拐点。 + +在2月1日，用户浏览了一个关于甲状腺术后康复过程的视频 <|video_begin|>。这个视频内容非常具体且带有一定的医疗严肃性，与她之前偏向轻松娱乐的内容消费习惯有显著差异。我推测，这个行为可能源于用户自身或身边亲友的健康状况变化，触发了她对特定健康问题的关注和焦虑。这不仅仅是泛泛的养生兴趣，而是由一个具体问题驱动的、带有明确指向性的信息搜集行为。 + +紧接着，在2月3日，用户的关注点从术后即时状态，深化到了长期的恢复与生活管理。她观看并评论了一个分享甲状腺手术四年后通过健康生活方式成功减重并保持积极心态的视频 <|video_begin|>。这个行为的递进性非常清晰：从了解“发生了什么”（术后监护），到探索“我该怎么做”（长期康复经验）。用户通过观看他人的成功案例，不仅在寻求心理上的共鸣和鼓励，更是在寻找一种可供自己参考和实践的、综合性的生活方式解决方案，这表明她的需求已经从单纯的信息获取升级为寻求可行的行动指南。 + +两天后的2月5日，用户的探索行为变得更加聚焦和专业化。她观看了一个由医生讲解的视频 <|video_begin|>，内容是关于富含特定营养素（硒）的水果对甲状腺健康的作用。这是一个关键的认知收敛过程。相比于前两天较为宽泛的“健康生活方式”，这次她开始关注具体的营养元素和精准的食疗方案。这说明用户已经不满足于笼统的建议，而是希望获得更具科学依据、更具象化的指导，比如“应该具体吃什么”。这个行为完成了从“问题认知”到“综合性方案探索”，再到“精准化对策学习”的完整认知闭环。 + +综合来看，我认为用户的行为展现了一个清晰的演化路径。她可能因为某个触发事件（浏览甲状腺术后视频 <|video_begin|>）产生了健康焦虑，随后通过观看他人长期的康复经验分享 <|video_begin|> 来寻求综合性的生活方式指导，最终将需求收敛到寻求医生讲解的、针对特定营养素的精准食疗方案 <|video_begin|>。这一系列行为表明，她对健康管理的认知正在从模糊走向清晰，从被动接收信息转向主动寻求专业的、可执行的解决方案，未来可能会对相关的健康食品、营养补充剂或更专业的医疗健康内容表现出较高的兴趣。 +{"logic_chain": {"name": "从健康焦虑到精准化饮食管理的认知演化", "events": [{"date": "2026-02-01", "action": "[视频-浏览] <|video_begin|>", "logic": "问题触发：通过浏览甲状腺术后康复视频，表现出对该健康问题的初步关注。"}, {"date": "2026-02-03", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣深化：从关注术后即时状态转向长期康复和生活方式管理，通过观看他人经验分享，寻求可行的实践路径。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知收敛与增量：从泛化的生活方式管理，聚焦到具体的营养素（硒）和食疗方案，完成了从“问题是什么”到“具体怎么做”的认知闭环，获得了明确的行动指导。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接手的任务是分析一位用户从被动接受情感短剧内容，到主动寻求人生哲理类书籍的行为转变。我的分析将主要依据其行为时间线，深入挖掘其内在动机和兴趣的演变过程。 + +首先，我注意到在2026年1月初，这位用户的行为模式发生了显著变化。在此之前，他的消费记录非常多样化，涵盖了从户外刀具<|prod_begin|>、食品<|prod_begin|>、日用品<|prod_begin|>到健康产品<|prod_begin|>等多个领域，展现出一种务实、注重生活品质的男性形象。然而，从1月5日开始，他的广告交互记录中密集地出现了大量情感纠葛、家庭伦理、婚姻危机主题的短剧广告。例如，他深度转化了关于离婚后家庭矛盾的《我们曾经有过家》<|ad_begin|>，以及后续多部类似主题的短剧，如《入狱后我执掌乾坤》<|ad_begin|>和《入赘五年，离婚即无敌》<|ad_begin|>。我推测，这种高频次、高强度的内容推送，让他沉浸在了一个充满戏剧性冲突和现实困境的虚拟环境中。这些短剧普遍探讨现代人在爱情、婚姻和自我价值实现中的挣扎，很可能触动了用户在现实生活中的某些思考或困惑。 + +紧接着，一个关键的转折点出现在了2026年1月9日。在连续几天被动接收了大量虚构的情感冲突故事后，我发现他的行为从“看”转向了“学”。他主动点击并购买了名为《渔樵问对》的书籍<|prod_begin|>，这本书的描述明确指向“解决人生困惑”和“感受国学经典智慧”。我认为这并非偶然。在被大量情感剧所营造的复杂问题包围后，用户可能不再满足于仅仅观看别人的故事，而是开始寻求一种能够指导自己、解决内心困惑的系统性知识。这标志着一次重要的认知跃迁：从被动地消费娱乐内容，转向主动地寻求智慧和答案。 + +这种寻求并未停止。在初步接触了宏观的人生哲理之后，用户的探索开始向更具体的领域深化。1月12日，他购买了另一本书籍《道家与性文化》<|prod_begin|>。在我看来，这是对他之前所接触的“情感纠葛”主题的一种更深层次的回应和探索。如果说《渔樵问对》是对普适性人生问题的思考，那么《道家与性文化》则将这种思考聚焦到了两性关系和个人身心修养这一更具体的层面。这表明他的兴趣正在从宽泛的哲学思考，收敛到与个人生活、特别是两性关系紧密相关的特定知识领域。 + +综合来看，我认为用户的行为轨迹清晰地展示了一个从问题沉浸到寻求解答，再到深化探索的完整闭环。他最初被动观看大量以离婚、家庭矛盾为主题的短剧广告，如《我们曾经有过家》<|ad_begin|>，这让他对复杂的情感关系问题产生了认知。随后，这种认知驱动他不再满足于观看故事，而是主动购买了旨在“解决人生困惑”的《渔樵问对》<|prod_begin|>，试图从哲学层面寻找答案。最后，他的探索进一步聚焦，通过购买《道家与性文化》<|prod_begin|>来深入研究与两性关系和个人修养相关的具体知识。因此，我倾向于认为，未来向他推荐兼具深度思考和实用性的国学、哲学或心理学内容，有较大概率被他所接纳。 +{"logic_chain": {"name": "从被动接受情感剧到主动寻求人生哲理解答的认知跃迁", "events": [{"date": "2026-01-06", "action": "[广告-深度转化] <|ad_begin|>", "logic": "问题域沉浸（触发阶段）：用户开始高密度地被动观看以“离婚”、“家庭矛盾”、“情感纠葛”为核心主题的短剧广告，构建了一个充满现实困境与情感挑战的认知背景。"}, {"date": "2026-01-09", "action": "[商品-购买] <|prod_begin|>", "logic": "行为模式转变与认知升级：在连续接触大量虚构的情感冲突后，用户行为从被动观看转为主动购买。购买的书籍《渔樵问对》明确标榜“解决人生困惑”，表明用户不再满足于观看表层故事，而是开始寻求更深层次、更具指导性的哲学智慧来解答现实或心理上的困惑，实现了从“看故事”到“找答案”的认知跃迁。"}, {"date": "2026-01-12", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣深化与领域聚焦：在寻求宏观人生哲理（《渔樵问对》）之后，用户的探索进一步具体化，转向了更细分的“道家与性文化”领域。这表明用户在初步的哲学思考后，开始关注与两性关系、个人养生相关的具体理论，是对前期“情感纠葛”主题的深度回应和探索，体现了从普适性智慧到特定领域知识的兴趣收敛。"}]}}<|im_end|> +面向婴幼儿的寝居用品，选用安全亲肤材质，兼具保暖透气防水等实用功能，设计童趣丰富，详细标注规格与卖点，且统一提供包邮服务。 +这款儿童床垫专为幼儿园及家庭宝宝设计，适用于午睡或日常睡眠，支持冬夏两用，四季皆宜。产品采用柔软舒适材质，表面为明亮黄色，边缘缝线规整，视觉上温馨活泼，适合儿童使用。标签显示其执行标准为GB/T 22796-2021，安全类别为GB 18401-2010A类，环保等级达E1级，保障健康安全。尺寸多样，提供50cm×130cm至88cm×168cm等多种规格，可适配不同儿童床。支持常规水洗，最高洗涤温度30℃，不可漂白、不可熨烫、不可悬挂晾干，维护便捷。作为自营商品，价格为58.2元，免运费，适合批发或家庭批量采购，是幼儿园、早教中心及家庭宝宝午休的理想选择。<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-08 02:37:29,455 >> 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] 2026-07-08 02:37:29,456 >> 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|2026-07-08 02:37:29] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-08 02:37:29] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-08 02:37:30,247 >> 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] 2026-07-08 02:37:30,247 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-08 02:37:30,304 >> 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] 2026-07-08 02:37:31,145 >> 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] 2026-07-08 02:37:31,544 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-08 02:37:31] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-08 02:37:31] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-08 02:37:31] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-08 02:37:31] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-08 02:37:31] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-08 02:37:31,568 >> 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] 2026-07-08 02:37:31,683 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-08 02:37:31,683 >> Num examples = 1,689 +[INFO|trainer.py:1471] 2026-07-08 02:37:31,683 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-08 02:37:31,683 >> Num update steps per epoch = 423 +[INFO|trainer.py:1473] 2026-07-08 02:37:31,683 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-08 02:37:31,683 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-08 02:37:31,683 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-08 02:37:31,683 >> Total optimization steps = 423 +[INFO|trainer.py:1479] 2026-07-08 02:37:31,684 >> Number of trainable parameters = 801,433,600 + 0%| | 0/423 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 423/423 [56:01<00:00, 6.46s/it] 100%|██████████| 423/423 [56:01<00:00, 7.95s/it] +[INFO|trainer.py:3815] 2026-07-08 03:33:32,917 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v17_item_caption_cot_lr13e6 +[INFO|configuration_utils.py:533] 2026-07-08 03:33:32,918 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v17_item_caption_cot_lr13e6/config.json +[INFO|configuration_utils.py:816] 2026-07-08 03:33:32,918 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v17_item_caption_cot_lr13e6/generation_config.json +{'loss': '2.977', 'grad_norm': '44.75', 'learning_rate': '4e-06', 'epoch': '0.01184'} +{'loss': '2.78', 'grad_norm': '12.25', 'learning_rate': '9e-06', 'epoch': '0.02368'} +{'loss': '2.531', 'grad_norm': '8.312', 'learning_rate': '1.3e-05', 'epoch': '0.03552'} +{'loss': '2.282', 'grad_norm': '5.062', 'learning_rate': '1.299e-05', 'epoch': '0.04737'} 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train_samples_per_second = 0.502 + train_steps_per_second = 0.126 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v17_item_caption_cot_lr13e6/training_loss.png +[WARNING|2026-07-08 03:33:33] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-08 03:33:33] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-08 03:33:34,000 >> 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..c328d81 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 2.0218118019963494e+17, + "train_loss": 1.6924350921143876, + "train_runtime": 3361.2318, + "train_samples_per_second": 0.502, + "train_steps_per_second": 0.126 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new 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