From 4bb27fb6f0bc2a2a003bef32e2b475f40f79a3f5 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 23 Aug 2026 13:01:17 +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/v05_user_item_up_lr2e5 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 + ...76321.ubuntu-System-Product-Name.1574564.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2389 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 147 + trainer_state.json | 1065 ++++++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 47726 bytes 16 files changed, 3903 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_02-32-01_ubuntu-System-Product-Name/events.out.tfevents.1783276321.ubuntu-System-Product-Name.1574564.0 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..ac7fca9 --- /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: v05_user_item_up_lr2e5 + results: [] +--- + + + +# v05_user_item_up_lr2e5 + +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_user_item_up 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: 2e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607065 +- 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..4b71957 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.5281465280347546e+17, + "train_loss": 1.4733744319910553, + "train_runtime": 6290.4242, + "train_samples_per_second": 0.465, + "train_steps_per_second": 0.116 +} \ 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..3394498 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:912c559b24c10118f8bc0a637821c72c4a0d2edef99bc8d05d3bb1013d6bda23 +size 1602902832 diff --git a/runs/Jul06_02-32-01_ubuntu-System-Product-Name/events.out.tfevents.1783276321.ubuntu-System-Product-Name.1574564.0 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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..73b9928 --- /dev/null +++ b/train.log @@ -0,0 +1,2389 @@ +/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 02:31:23] 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 02:31:24,479 >> 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 02:31:24,481 >> 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 02:31:28,577 >> 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 02:31:28,578 >> 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 02:31:28,947 >> 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 02:31:28,947 >> 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 02:31:30] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/20260706_overnight/data/user_item_up.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: 11815 examples [00:00, 111111.94 examples/s] Generating train split: 26125 examples [00:00, 122783.96 examples/s] Generating train split: 42758 examples [00:00, 134713.57 examples/s] Generating train split: 51540 examples [00:00, 132956.70 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/51540 [00:00user +【用户交互历史】： +【2024-09-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-26】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-27】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-29】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-01】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-03】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + 10:50 [广告-深度转化] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 15:17 [广告-深度转化] <|ad_begin|> + 15:55 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + 02:09 [广告-深度转化] <|ad_begin|> + 02:13 [广告-深度转化] <|ad_begin|> + 02:40 [广告-深度转化] <|ad_begin|> + 04:18 [广告-深度转化] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-08】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 11:32 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 10:57 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-14】 + --:-- [直播-关注/打赏] <|living_begin|> +【2026-01-15】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> +【2026-01-18】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-19】 + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-20】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-21】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 10:11 [广告-深度转化] <|ad_begin|> + 10:18 [广告-深度转化] <|ad_begin|> + 12:19 [广告-深度转化] <|ad_begin|> + 12:20 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:31 [广告-深度转化] <|ad_begin|> + 13:38 [广告-深度转化] <|ad_begin|> + 13:39 [广告-深度转化] <|ad_begin|> + 13:43 [广告-深度转化] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:08 [广告-深度转化] <|ad_begin|> + 14:17 [广告-深度转化] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:39 [广告-深度转化] <|ad_begin|> + 14:44 [广告-深度转化] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:09 [广告-深度转化] <|ad_begin|> + 15:10 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + 01:29 [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:32 [广告-深度转化] <|ad_begin|> + 04:36 [广告-深度转化] <|ad_begin|> + 04:36 [广告-深度转化] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 17:20 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 08:26 [视频-长播] <|video_begin|> + 11:42 [广告-深度转化] <|ad_begin|> + 13:27 [视频-长播] <|video_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + --:-- [直播-关注/打赏] <|living_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|> + 07:38 [视频-长播] <|video_begin|> + 09:52 [视频-长播] <|video_begin|> + 10:39 [广告-深度转化] <|ad_begin|> + 11:44 [广告-深度转化] <|ad_begin|> + 15:57 [广告-深度转化] <|ad_begin|> + 15:58 [广告-深度转化] <|ad_begin|> + 15:59 [广告-深度转化] <|ad_begin|> + 17:12 [视频-长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:12 [视频-长播] <|video_begin|> + 00:43 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:55 [广告-深度转化] <|ad_begin|> + 03:09 [视频-长播] <|video_begin|> + 03:33 [视频-长播] <|video_begin|> + 03:33 [视频-长播] <|video_begin|> + 05:20 [广告-深度转化] <|ad_begin|> + 10:54 [广告-深度转化] <|ad_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 12:37 [视频-长播] <|video_begin|> + 13:48 [广告-深度转化] <|ad_begin|> + 14:17 [广告-深度转化] <|ad_begin|> + 14:19 [广告-深度转化] <|ad_begin|> + 14:20 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + 01:01 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:55 [视频-长播] <|video_begin|> + 21:55 [视频-长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + 03:50 [视频-长播] <|video_begin|> + 04:32 [视频-长播] <|video_begin|> + 04:58 [广告-深度转化] <|ad_begin|> + 10:16 [广告-深度转化] <|ad_begin|> + 10:28 [视频-长播] <|video_begin|> +【2026-01-29】 + --:-- [直播-首次打赏] <|living_begin|> + 01:18 [视频-长播] <|video_begin|> + 01:18 [视频-长播] <|video_begin|> + 08:01 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注/打赏] <|living_begin|> + 03:38 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:53 [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|> + 07:16 [视频-长播] <|video_begin|> + 08:27 [视频-长播] <|video_begin|> + 08:27 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:10 [视频-长播] <|video_begin|> + 16:23 [视频-长播] <|video_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:39 [视频-长播] <|video_begin|> + 17:01 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:06 [视频-长播] <|video_begin|> + 04:33 [广告-深度转化] <|ad_begin|> + 13:57 [视频-长播] <|video_begin|> + 13:57 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> + 16:00 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:30 [视频-长播] <|video_begin|> + 00:30 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:47 [视频-长播] <|video_begin|> + 15:13 [广告-深度转化] <|ad_begin|> + 15:14 [广告-深度转化] <|ad_begin|> + 16:06 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:19 [视频-长播] <|video_begin|> + 01:19 [视频-长播] <|video_begin|> + 08:54 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 09:00 [广告-深度转化] <|ad_begin|> + 11:16 [广告-深度转化] <|ad_begin|> + 13:40 [广告-深度转化] <|ad_begin|> + 15:59 [广告-深度转化] <|ad_begin|> + 16:21 [视频-长播] <|video_begin|> + 16:21 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:43 [广告-点击] <|ad_begin|> + 00:57 [广告-深度转化] <|ad_begin|> + 00:57 [广告-点击] <|ad_begin|> + 01:02 [广告-点击] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 01:08 [广告-点击] <|ad_begin|> + 01:09 [广告-点击] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 01:33 [广告-点击] <|ad_begin|> + 01:50 [广告-点击] <|ad_begin|> + 01:58 [广告-点击] <|ad_begin|> + 02:00 [广告-点击] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 04:03 [广告-点击] <|ad_begin|> + 04:04 [广告-点击] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 04:43 [广告-点击] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 04:46 [广告-深度转化] <|ad_begin|> + 04:46 [广告-点击] <|ad_begin|> + 04:47 [广告-点击] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 04:50 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:12 [广告-点击] <|ad_begin|> + 05:13 [广告-点击] <|ad_begin|> + 05:15 [广告-点击] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 07:31 [广告-点击] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 08:32 [广告-点击] <|ad_begin|> + 09:03 [广告-点击] <|ad_begin|> + 09:05 [广告-点击] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + 09:13 [广告-点击] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 09:48 [视频-长播] <|video_begin|> + 10:15 [广告-点击] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> + 11:08 [广告-点击] <|ad_begin|> + 11:12 [广告-点击] <|ad_begin|> + 11:14 [广告-点击] <|ad_begin|> + 11:15 [广告-深度转化] <|ad_begin|> + 11:15 [广告-点击] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 11:17 [广告-点击] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:35 [广告-点击] <|ad_begin|> + 11:42 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 11:57 [广告-点击] <|ad_begin|> + 12:01 [广告-点击] <|ad_begin|> + 12:03 [广告-点击] <|ad_begin|> + 12:10 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:21 [广告-点击] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 14:27 [广告-点击] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 15:15 [广告-点击] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> + 15:59 [广告-点击] <|ad_begin|> + 15:59 [广告-点击] <|ad_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家与数据格式化专家，请依据以上用户交互历史，围绕给定主题提取具备递进关系和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心判定逻辑（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 必须在同一生活场景、主题类目下具有极强的需求补完关系。 +2.兴趣因果递进：交互 B 必须由交互 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。 +请针对以下主题提取行为逻辑链： +主题：从娱乐消遣到变现探索的认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从娱乐消遣到变现探索的认知演化", + "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。任务要求我基于用户的交互历史，挖掘出一条从“娱乐消遣”到“变现探索”的行为逻辑链。我需要以第一人称的分析师口吻，通过对时间线的解读，归纳出用户的兴趣迁移和动机变化，并最终总结出一条符合要求的行为链。 + +我首先浏览了用户的完整行为历史。在2025年12月之前，我注意到用户的消费行为主要集中在购买各类生活用品和食品上，例如购买鹿茸菇 <|prod_begin|>、洗衣粉 <|prod_begin|> 和腊肠 <|prod_begin|> 等，这反映了一个典型的居家生活消费者的画像。 + +转折点出现在2025年12月8日，用户开始密集地关注和打赏直播。我仔细分析了这一阶段的行为，发现初期用户的兴趣非常明确，集中在娱乐消遣上。例如，从12月8日到12月28日，他关注和打赏的主播绝大多数是高颜值、好身材、能歌善舞的才艺型女主播。典型的例子是他在12月15日首次打赏的主播 <|living_begin|>，其标签是“大眼睛”、“大长腿”、“唱歌”、“情感歌曲”等。这一系列行为表明，用户最初进入直播场景，其核心动机是寻求视听娱乐和情感陪伴，这是一种纯粹的内容消费行为。 + +然而，一个关键的兴趣转变信号出现在2025年12月29日。在连续打赏了多位才艺型主播后，用户首次打赏了一个非常特别的直播间 <|living_begin|>。我注意到这个直播间的标签是“不露脸直播”、“快手极速版活跃教学”和“金币赚取攻略”。这与他之前的所有直播偏好都截然不同。这次打赏行为让我推测，用户的动机发生了显著变化。他不再仅仅满足于被动地观看和消费内容，而是开始主动探索平台本身的玩法机制，特别是那些与“赚钱”、“获利”相关的攻略。这标志着他从一个纯粹的娱乐消费者，开始向一个平台玩法探索者转变，认知上实现了从“花钱娱乐”到“研究如何赚钱”的跃迁。 + +为了验证这个推测，我继续向后追溯他的行为。果然，在2026年1月8日，我发现用户再次打赏了一个同类型的直播间 <|living_begin|>。这个直播间同样是“不露脸”的，核心内容是“快手极速版干货分享”、“签到打卡教学”和“金币攻略科普”。这次重复的打赏行为有力地印证了我的判断。这表明他对平台变现机制的兴趣并非一时兴起，而是已经形成了一个持续、稳定的关注点。他正在主动、深入地学习如何通过平台的各种任务和技巧来获取收益。 + +综合以上分析，我看到了一条清晰的认知演化路径。用户最初被直播的娱乐属性吸引，进行消费和打赏。但在这个过程中，他逐渐对平台内部的激励和变现机制产生了好奇，并开始主动学习和探索如何从中获利。这种从被动娱乐到主动探索变现的转变，是用户在平台生态中认知深化的重要体现。因此，我倾向于认为，后续可以向他推荐更多关于平台玩法、任务攻略或轻量级变现技巧的内容，有较大概率会吸引他的关注。 + +根据我的分析，我将这条行为逻辑链总结如下： +用户的行为起点是典型的娱乐消费，如在2025年12月15日打赏高颜值才艺型主播 <|living_begin|>。随后，在2025年12月29日，他首次打赏了一个教授“金币赚取攻略”和“账号权重科普”的教学型直播 <|living_begin|>，这标志着他的认知从纯粹的娱乐消费转向对平台变现机制的探索。最后，在2026年1月8日，他再次打赏了同类型的“快手极速版干货分享”直播 <|living_begin|>，进一步学习“停留互动技巧”，这表明他对该领域的兴趣得到了确认并持续深化。 +{"logic_chain": {"name": "从娱乐消遣到变现探索的认知演化", "events": [{"date": "2025-12-15", "action": "[直播-首次打赏] <|living_begin|>", "logic": "初始阶段：用户行为以典型的娱乐消费为主，关注并打赏高颜值、才艺型女主播，属于被动内容消费。"}, {"date": "2025-12-29", "action": "[直播-首次打赏] <|living_begin|>", "logic": "认知跃迁：用户首次打赏一个“不露脸”的教学型直播，其核心内容是“金币赚取攻略”和“账号权重科普”。这标志着用户从纯粹的观众，转变为对平台玩法和变现机制产生兴趣的探索者，实现了从“消费”到“生产/获利”的认知转变。"}, {"date": "2026-01-08", "action": "[直播-首次打赏] <|living_begin|>", "logic": "行为强化与深化：用户再次打赏同类型的“快手极速版干货分享”直播，进一步关注“停留互动技巧”等更深层次的玩法。这表明其对该领域的兴趣得到确认并持续深化，正在主动学习如何更有效地参与平台活动。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-08-18】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-19】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-30】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-31】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-15】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-24】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-26】 + --:-- [直播-关注] <|living_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [直播-关注] <|living_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [直播-关注] <|living_begin|> +【2025-12-17】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-24】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-25】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-31】 + --:-- [直播-打赏] <|living_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> +【2026-01-13】 + 01:19 [广告-深度转化] <|ad_begin|> + 03:04 [广告-深度转化] <|ad_begin|> + 13:31 [广告-深度转化] <|ad_begin|> + 21:38 [广告-深度转化] <|ad_begin|> + 22:53 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:28 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:06 [广告-深度转化] <|ad_begin|> + 02:59 [广告-深度转化] <|ad_begin|> + 21:25 [广告-深度转化] <|ad_begin|> + 22:45 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 23:39 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 17:17 [广告-深度转化] <|ad_begin|> + 20:25 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + 18:13 [广告-深度转化] <|ad_begin|> + 20:49 [广告-深度转化] <|ad_begin|> + 23:12 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 21:45 [广告-深度转化] <|ad_begin|> + 23:05 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 20:40 [广告-深度转化] <|ad_begin|> + 22:38 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + 02:06 [广告-深度转化] <|ad_begin|> + 04:39 [视频-长播] <|video_begin|> + 04:39 [视频-长播] <|video_begin|> + 07:35 [视频-长播] <|video_begin|> + 07:35 [视频-长播] <|video_begin|> + 07:35 [视频-长播] <|video_begin|> + 07:35 [视频-长播] <|video_begin|> + 07:54 [广告-深度转化] <|ad_begin|> + 09:46 [广告-深度转化] <|ad_begin|> + 18:11 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:56 [视频-长播] <|video_begin|> + 21:12 [视频-长播] <|video_begin|> + 21:15 [视频-长播] <|video_begin|> +【2026-01-23】 + 06:59 [广告-深度转化] <|ad_begin|> + 19:48 [广告-深度转化] <|ad_begin|> + 22:40 [广告-深度转化] <|ad_begin|> + 23:47 [视频-长播] <|video_begin|> +【2026-01-24】 + 00:45 [视频-长播] <|video_begin|> + 00:45 [视频-长播] <|video_begin|> + 00:49 [视频-长播] <|video_begin|> + 00:49 [视频-长播] <|video_begin|> + 00:49 [视频-长播] <|video_begin|> + 02:40 [视频-长播] <|video_begin|> + 07:31 [广告-深度转化] <|ad_begin|> + 08:14 [广告-深度转化] <|ad_begin|> + 20:52 [视频-长播] <|video_begin|> + 22:44 [广告-深度转化] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 00:11 [视频-长播] <|video_begin|> + 07:36 [视频-长播] <|video_begin|> + 09:02 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + 08:25 [广告-深度转化] <|ad_begin|> + 17:48 [广告-深度转化] <|ad_begin|> + 17:48 [广告-点击] <|ad_begin|> + 20:01 [广告-深度转化] <|ad_begin|> + 20:01 [广告-点击] <|ad_begin|> + 20:08 [视频-评论/长播] <|video_begin|> + 20:59 [广告-点击] <|ad_begin|> + 21:34 [广告-点击] <|ad_begin|> + 22:01 [广告-深度转化] <|ad_begin|> + 22:01 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:43 [广告-深度转化] <|ad_begin|> + 06:43 [广告-点击] <|ad_begin|> + 10:26 [视频-长播] <|video_begin|> + 17:09 [广告-深度转化] <|ad_begin|> + 17:09 [广告-点击] <|ad_begin|> + 18:22 [广告-深度转化] <|ad_begin|> + 18:22 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:26 [广告-点击] <|ad_begin|> + 21:34 [广告-深度转化] <|ad_begin|> + 21:34 [广告-点击] <|ad_begin|> +【2026-01-28】 + 15:58 [视频-长播] <|video_begin|> + 17:52 [视频-长播] <|video_begin|> + 18:00 [视频-长播] <|video_begin|> + 18:00 [视频-长播] <|video_begin|> + 18:06 [视频-长播] <|video_begin|> + 18:38 [广告-点击] <|ad_begin|> + 18:41 [视频-长播] <|video_begin|> + 18:49 [视频-长播] <|video_begin|> + 19:18 [广告-深度转化] <|ad_begin|> + 19:18 [广告-点击] <|ad_begin|> + 21:33 [视频-长播] <|video_begin|> + 21:33 [视频-长播] <|video_begin|> + 21:54 [广告-深度转化] <|ad_begin|> + 21:54 [广告-点击] <|ad_begin|> +【2026-01-29】 + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + 17:31 [视频-长播] <|video_begin|> + 18:16 [广告-深度转化] <|ad_begin|> + 18:16 [广告-点击] <|ad_begin|> + 22:01 [广告-点击] <|ad_begin|> + 22:21 [广告-深度转化] <|ad_begin|> + 22:21 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:20 [广告-深度转化] <|ad_begin|> + 00:20 [广告-点击] <|ad_begin|> + 02:06 [广告-深度转化] <|ad_begin|> + 02:06 [广告-点击] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> + 07:06 [广告-点击] <|ad_begin|> + 07:23 [广告-深度转化] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 11:36 [广告-深度转化] <|ad_begin|> + 11:36 [广告-点击] <|ad_begin|> + 19:28 [广告-深度转化] <|ad_begin|> + 19:28 [广告-点击] <|ad_begin|> + 21:51 [广告-点击] <|ad_begin|> + 21:59 [广告-点击] <|ad_begin|> + 22:39 [广告-深度转化] <|ad_begin|> + 22:39 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [直播-打赏] <|living_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 17:01 [广告-点击] <|ad_begin|> + 17:07 [广告-点击] <|ad_begin|> + 17:07 [视频-长播] <|video_begin|> + 17:21 [广告-深度转化] <|ad_begin|> + 17:21 [广告-点击] <|ad_begin|> + 18:17 [广告-深度转化] <|ad_begin|> + 18:17 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:28 [视频-长播] <|video_begin|> + 21:28 [广告-深度转化] <|ad_begin|> + 21:28 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + 01:39 [广告-点击] <|ad_begin|> + 02:46 [视频-长播] <|video_begin|> + 02:46 [视频-长播] <|video_begin|> + 02:56 [视频-长播] <|video_begin|> + 02:56 [视频-长播] <|video_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 07:21 [广告-点击] <|ad_begin|> + 07:25 [广告-点击] <|ad_begin|> + 09:45 [广告-点击] <|ad_begin|> + 15:14 [广告-点击] <|ad_begin|> + 15:19 [广告-深度转化] <|ad_begin|> + 15:19 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 19:48 [广告-点击] <|ad_begin|> + 21:32 [广告-深度转化] <|ad_begin|> + 21:32 [广告-点击] <|ad_begin|> + 22:52 [广告-深度转化] <|ad_begin|> + 22:52 [广告-点击] <|ad_begin|> +【2026-02-02】 + 07:56 [视频-长播] <|video_begin|> + 08:01 [广告-点击] <|ad_begin|> + 09:52 [广告-深度转化] <|ad_begin|> + 09:52 [广告-点击] <|ad_begin|> + 15:18 [广告-点击] <|ad_begin|> + 18:57 [视频-长播] <|video_begin|> + 18:59 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:26 [广告-点击] <|ad_begin|> + 21:29 [广告-点击] <|ad_begin|> + 21:36 [广告-深度转化] <|ad_begin|> + 21:36 [广告-点击] <|ad_begin|> +【2026-02-03】 + 07:41 [广告-深度转化] <|ad_begin|> + 07:41 [广告-点击] <|ad_begin|> + 10:01 [广告-深度转化] <|ad_begin|> + 10:01 [广告-点击] <|ad_begin|> + 16:17 [广告-深度转化] <|ad_begin|> + 16:17 [广告-点击] <|ad_begin|> + 17:15 [广告-点击] <|ad_begin|> + 17:50 [广告-深度转化] <|ad_begin|> + 17:50 [广告-点击] <|ad_begin|> + 19:06 [广告-深度转化] <|ad_begin|> + 19:06 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + --:-- [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|> + 19:16 [广告-点击] <|ad_begin|> + 19:18 [广告-点击] <|ad_begin|> + 21:17 [广告-深度转化] <|ad_begin|> + 21:17 [广告-点击] <|ad_begin|> +【2026-02-04】 + 09:45 [广告-深度转化] <|ad_begin|> + 09:45 [广告-点击] <|ad_begin|> + 10:19 [广告-点击] <|ad_begin|> + 14:43 [视频-长播] <|video_begin|> + 14:44 [广告-点击] <|ad_begin|> + 14:46 [视频-长播] <|video_begin|> + 14:46 [视频-长播] <|video_begin|> + 15:31 [广告-深度转化] <|ad_begin|> + 15:31 [广告-点击] <|ad_begin|> + 18:29 [视频-长播] <|video_begin|> + 18:29 [视频-长播] <|video_begin|> + 18:37 [广告-深度转化] <|ad_begin|> + 18:37 [广告-点击] <|ad_begin|> + 18:57 [广告-深度转化] <|ad_begin|> + 18:57 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 19:53 [广告-点击] <|ad_begin|> + 20:59 [视频-长播] <|video_begin|> + 21:03 [视频-长播] <|video_begin|> + 21:12 [广告-深度转化] <|ad_begin|> + 21:12 [广告-点击] <|ad_begin|> +【2026-02-05】 + 02:56 [视频-长播] <|video_begin|> + 06:49 [广告-点击] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 09:16 [广告-深度转化] <|ad_begin|> + 09:16 [广告-点击] <|ad_begin|> + 09:18 [视频-长播] <|video_begin|> + 09:20 [视频-长播] <|video_begin|> +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，根据以上用户交互历史，针对目标主题抽取兼具行为递进性与内在关联性的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +1.场景驱动协同：交互 B 与 交互 A 必须在同一生活场景、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 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请针对下列主题提取行为逻辑链： +主题：从被动获取信息到主动购买决策的厨具升级路径 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动获取信息到主动购买决策的厨具升级路径", + "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，我来分析一下这项推荐数据挖掘任务。我的任务是基于用户的交互历史，挖掘出一条从被动获取信息到主动进行厨具升级决策的行为路径。 + +我首先梳理了用户的整体行为时间线，发现他在2026年1月22日这天的行为序列特别值得关注。在此之前，用户的消费和直播偏好主要集中在服饰、零食以及观看美女、才艺型主播上，与厨房用品的关联度并不高。然而，在1月22日，他的行为出现了一个明显的兴趣焦点转移。 + +当天，我注意到他观看了一个关于多品牌菜刀测评的视频 <|video_begin|>。这个视频详细对比了张小泉等多个品牌刀具的性能，这很可能首次激发了他对厨房工具专业性的认知。通常，这类测评内容会引发用户对自己现有工具的审视。紧接着，一个非常有趣的决策发生了：他并没有立刻购买一把新刀，而是购买了一件与视频中提到的品牌“张小泉”相关的商品——一个多功能磨刀器 <|prod_begin|>。我推测，这是他在接收到新信息后，采取的一种低成本、高效率的解决方案，试图先通过维护来提升现有刀具的性能，这是一种非常务实的行为。 + +这个行为链并没有就此中断。在解决了刀具锋利度这个基础问题后，我发现他的兴趣立刻发生了延伸和升级。同一天，他又观看了一个关于多品牌破壁机的视频 <|video_begin|>。这个视频展示了破壁机在制作健康饮品方面的便捷性和多功能性。从关注单一功能的切割工具（菜刀），到关注复合功能的厨房电器（破壁机），这在我看来是一次显著的认知跃迁。他不再仅仅满足于基础的烹饪准备工作，而是开始追求更高层次的、便捷化的健康生活方式，比如制作营养早餐。这个兴趣的迁移路径非常清晰，从一个具体问题的解决，自然地过渡到了对整个厨房生活品质提升的向往。 + +综合来看，我认为用户的行为展现了一个从被动信息接收到主动决策的完整闭环。他先是通过观看菜刀测评视频 <|video_begin|> 意识到厨具性能的重要性，随即购买了张小泉磨刀器 <|prod_begin|> 来优化现有工具，紧接着兴趣点又被另一个视频 <|video_begin|> 引导至功能更强大的破壁机上。这一系列行为逻辑紧密，层层递进，清晰地勾勒出他对于厨具升级的思考路径。因此，未来向他推荐高品质、多功能的厨房小家电，可能会获得不错的反馈。 +{"logic_chain": {"name": "从被动获取信息到主动购买决策的厨具升级路径", "events": [{"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "问题触发与知识获取：通过观看多品牌菜刀测评视频，触发了对厨房刀具性能的关注和认知升级的需求。"}, {"date": "2026-01-22", "action": "[商品-购买] <|prod_begin|>", "logic": "行为修正与实践：基于对刀具锋利度的认知，用户没有直接购买新刀，而是选择购买与视频中品牌“张小泉”相关的辅助工具——磨刀器，作为提升现有工具性能的低成本解决方案。"}, {"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣延伸与认知跃迁：在解决了刀具问题后，兴趣从单一的切割工具延伸到功能更复杂的厨房电器（破壁机），体现了从基础工具维护到追求更高生活品质（便捷健康早餐）的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-13】 + --:-- [直播-关注] <|living_begin|> +【2025-01-26】 + --:-- [直播-关注] <|living_begin|> +【2025-02-18】 + --:-- [直播-关注] <|living_begin|> +【2025-02-24】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-18】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-04】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-05】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-06】 + 02:47 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:16 [广告-深度转化] <|ad_begin|> + 10:55 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 00:12 [广告-深度转化] <|ad_begin|> + 10:09 [广告-深度转化] <|ad_begin|> + 10:09 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> + 08:22 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-11】 + 00:55 [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|> + 09:18 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 11:36 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 08:30 [广告-深度转化] <|ad_begin|> + 09:33 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 06:31 [广告-深度转化] <|ad_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 11:23 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 22:48 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 07:24 [广告-深度转化] <|ad_begin|> + 08:06 [广告-深度转化] <|ad_begin|> + 10:21 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 07:46 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + 08:23 [广告-深度转化] <|ad_begin|> + 09:52 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:47 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 07:12 [广告-深度转化] <|ad_begin|> + 09:32 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + 00:21 [广告-深度转化] <|ad_begin|> + 00:39 [广告-深度转化] <|ad_begin|> + 02:46 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 08:32 [广告-深度转化] <|ad_begin|> + 08:56 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + 07:28 [广告-深度转化] <|ad_begin|> + 08:34 [广告-深度转化] <|ad_begin|> + 14:17 [广告-深度转化] <|ad_begin|> + 23:27 [视频-浏览] <|video_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:03 [广告-点击] <|ad_begin|> + 02:24 [广告-深度转化] <|ad_begin|> + 02:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:02 [广告-深度转化] <|ad_begin|> + 03:02 [广告-点击] <|ad_begin|> + 05:15 [广告-点击] <|ad_begin|> + 05:24 [广告-深度转化] <|ad_begin|> + 05:24 [广告-点击] <|ad_begin|> + 06:20 [视频-点赞/长播] <|video_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:30 [广告-点击] <|ad_begin|> + 07:43 [广告-点击] <|ad_begin|> + 08:51 [广告-深度转化] <|ad_begin|> + 08:51 [广告-点击] <|ad_begin|> + 09:26 [广告-点击] <|ad_begin|> + 09:33 [广告-点击] <|ad_begin|> + 09:37 [广告-点击] <|ad_begin|> + 09:47 [广告-点击] <|ad_begin|> + 10:08 [广告-深度转化] <|ad_begin|> + 10:08 [广告-点击] <|ad_begin|> + 10:19 [广告-点击] <|ad_begin|> + 10:47 [广告-点击] <|ad_begin|> + 13:17 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 00:45 [广告-深度转化] <|ad_begin|> + 00:45 [广告-点击] <|ad_begin|> + 01:01 [广告-点击] <|ad_begin|> + 01:15 [广告-点击] <|ad_begin|> + 01:26 [广告-点击] <|ad_begin|> + 09:21 [广告-深度转化] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 10:38 [广告-点击] <|ad_begin|> + 10:41 [广告-点击] <|ad_begin|> +【2026-01-25】 + 00:25 [广告-点击] <|ad_begin|> + 05:27 [视频-关注/长播] <|video_begin|> + 05:58 [广告-点击] <|ad_begin|> + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> + 09:02 [广告-点击] <|ad_begin|> + 09:09 [广告-深度转化] <|ad_begin|> + 09:09 [广告-点击] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> +【2026-01-26】 + 06:31 [广告-深度转化] <|ad_begin|> + 06:31 [广告-点击] <|ad_begin|> + 06:34 [广告-点击] <|ad_begin|> + 06:58 [广告-深度转化] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 07:42 [广告-深度转化] <|ad_begin|> + 07:42 [广告-点击] <|ad_begin|> + 07:58 [广告-深度转化] <|ad_begin|> + 07:58 [广告-点击] <|ad_begin|> + 10:24 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 00:48 [广告-深度转化] <|ad_begin|> + 00:48 [广告-点击] <|ad_begin|> + 04:21 [广告-深度转化] <|ad_begin|> + 04:21 [广告-点击] <|ad_begin|> + 06:44 [广告-深度转化] <|ad_begin|> + 06:44 [广告-点击] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> +【2026-01-28】 + 00:46 [视频-浏览] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:39 [广告-深度转化] <|ad_begin|> + 03:39 [广告-点击] <|ad_begin|> + 04:28 [广告-点击] <|ad_begin|> + 05:12 [视频-浏览] <|video_begin|> + 06:20 [广告-点击] <|ad_begin|> +【2026-01-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:37 [广告-点击] <|ad_begin|> + 02:42 [广告-深度转化] <|ad_begin|> + 02:42 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:52 [广告-点击] <|ad_begin|> + 03:12 [广告-点击] <|ad_begin|> + 03:38 [广告-点击] <|ad_begin|> + 03:53 [广告-深度转化] <|ad_begin|> + 03:53 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:26 [广告-点击] <|ad_begin|> + 06:28 [广告-深度转化] <|ad_begin|> + 06:28 [广告-点击] <|ad_begin|> + 09:21 [广告-深度转化] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [直播-首次打赏] <|living_begin|> + 02:38 [广告-点击] <|ad_begin|> + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 06:14 [广告-点击] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:39 [广告-深度转化] <|ad_begin|> + 08:39 [广告-点击] <|ad_begin|> +【2026-02-01】 + 02:37 [广告-深度转化] <|ad_begin|> + 02:37 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:19 [广告-深度转化] <|ad_begin|> + 04:19 [广告-点击] <|ad_begin|> + 04:53 [广告-深度转化] <|ad_begin|> + 04:53 [广告-点击] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:57 [广告-点击] <|ad_begin|> + 09:04 [广告-点击] <|ad_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-03】 + 02:34 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:46 [广告-深度转化] <|ad_begin|> + 05:46 [广告-点击] <|ad_begin|> + 07:36 [视频-长播] <|video_begin|> + 07:36 [视频-长播] <|video_begin|> + 07:38 [视频-长播] <|video_begin|> + 07:38 [视频-长播] <|video_begin|> + 09:39 [视频-长播] <|video_begin|> + 10:49 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:42 [视频-长播] <|video_begin|> + 22:45 [视频-长播] <|video_begin|> + 22:46 [视频-长播] <|video_begin|> + 22:48 [视频-长播] <|video_begin|> + 22:48 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:41 [视频-长播] <|video_begin|> + 02:41 [视频-长播] <|video_begin|> + 02:41 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 02:58 [视频-长播] <|video_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:16 [广告-点击] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> + 08:23 [视频-长播] <|video_begin|> + 08:23 [视频-长播] <|video_begin|> + 08:23 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:47 [广告-深度转化] <|ad_begin|> + 08:47 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:21 [视频-长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:14 [视频-长播] <|video_begin|> + 01:46 [视频-长播] <|video_begin|> + 01:50 [视频-长播] <|video_begin|> + 01:53 [视频-长播] <|video_begin|> + 02:32 [广告-深度转化] <|ad_begin|> + 02:32 [广告-点击] <|ad_begin|> + 04:39 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 04:41 [视频-长播] <|video_begin|> + 04:42 [视频-长播] <|video_begin|> + 04:45 [视频-长播] <|video_begin|> + 04:45 [视频-长播] <|video_begin|> + 04:45 [视频-长播] <|video_begin|> + 04:50 [视频-长播] <|video_begin|> + 04:50 [视频-长播] <|video_begin|> + 04:50 [视频-长播] <|video_begin|> + 05:03 [广告-点击] <|ad_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:30 [视频-长播] <|video_begin|> + 07:30 [视频-长播] <|video_begin|> + 07:40 [广告-深度转化] <|ad_begin|> + 07:40 [广告-点击] <|ad_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家与数据格式化专家，请依据以上用户交互历史，围绕给定主题提取具备递进关系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)： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +1.场景驱动协同：交互 B 与 交互 A 必须在同一生活场景、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 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +3.需求深度细化：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从传统建筑风水到现代自建房设计的落地探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从传统建筑风水到现代自建房设计的落地探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我将以推荐分析师的视角，对这位用户的行为数据进行解读。 + +我的任务是基于用户的交互历史，挖掘出一条从传统建筑风水兴趣到现代自建房设计探索的行为逻辑链。 + +在分析这份长达一年的交互记录时，我首先注意到用户在2025年展现出非常务实的生活消费偏好。他大量购买了食品、农产品、五金工具以及一些农机配件，例如防冻液<|prod_begin|>、钢丝钳<|prod_begin|>、播种机配件<|prod_begin|>和开垦刀<|prod_begin|>。同时，他关注的直播内容也多为接地气的草根歌手、时政分析和农机讲解<|living_begin|>，这些都描绘出一个关注实际生活、可能从事或熟悉农业/体力劳动的用户画像。 + +然而，进入2026年1月下旬，我观察到用户行为出现了一个非常有趣的转变。在1月28日，他浏览了一个关于北方建房风水理念的视频<|video_begin|>。这个视频探讨了如何利用“子孙道”等传统风水概念来优化空间布局。这对我来说是一个关键信号，表明用户可能产生了建房或改造房屋的初步想法，并且他的兴趣起点并非现代建筑设计，而是根植于传统文化和风水观念。 + +这个兴趣点在接下来的一周内迅速发酵并具体化。到了2月5日，我看到他密集地观看了一系列与建房相关的视频。其中一个视频<|video_begin|>展示了中式传统风格的乡村自建房模型，这恰好承接了他之前对风水理念的关注，将抽象的布局概念与具象的“中式传统建筑”实体联系了起来。这说明他的想法正在从“怎么建”的理论层面，发展到“建成什么样”的视觉化阶段。 + +紧接着，就在同一天，他的关注点进一步下沉到了具体的建筑构件和材料上。他观看了一个详细介绍铝合金影壁墙的视频<|video_begin|>。影壁本身就是中式建筑和风水布局中的一个标志性元素，具有保护隐私、挡风聚气的功能。而他关注的材质是“铝合金”，这是一种现代工业材料。这个行为非常有价值，因为它清晰地展示了用户正在试图将传统建筑元素（影壁）与现代工艺技术（铝合金）相结合，寻求一个兼顾传统美学与现代实用性的解决方案。这标志着他的探索已经从宏观的风格和理念，收敛到了具体部件的技术选型和落地实施层面。 + +综合来看，我认为用户的行为展现了一条清晰的递进逻辑。他从对传统建房风水的抽象兴趣出发，逐步将这种兴趣具象化为对中式乡村自建房模型的关注，最终聚焦到如何用现代材料实现传统建筑元素这一具体问题上。 + +因此，我推断用户的核心需求演变路径如下： +最初，用户在2026年1月28日因浏览关于北方建房风水理念和空间布局的视频<|video_begin|>，触发了建房的初步兴趣。随后在2月5日，这一兴趣变得更加具体，他开始观看中式传统建筑风格的乡村自建房模型视频<|video_begin|>，将抽象的风水概念与实际的建筑形态联系起来。最终，他的需求进一步细化，聚焦于具体的建筑构件和现代工艺，通过观看关于铝合金影壁墙设计与安装的视频<|video_begin|>，探索如何将传统元素与现代材料结合，进入了实际选材和技术考量的阶段。基于此，未来向他推荐融合传统设计与现代建材的自建房方案、相关施工工艺或建材品牌，有较大概率被他关注。 +{"logic_chain": {"name": "从传统建筑风水到现代自建房设计的落地探索", "events": [{"date": "2026-01-28", "action": "[视频-浏览] <|video_begin|>", "logic": "初始触发：用户对“建房风水理念”产生兴趣，关注如何通过传统理念优化“空间布局”，这是建房需求的抽象起点。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知具象化：用户的兴趣从抽象的风水概念，转变为对“中式传统建筑风格”的“乡村自建房屋模型”的具象化探索，开始将理念与实际建筑形态相结合。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "决策收敛与技术聚焦：用户进一步将注意力聚焦到“铝合金影壁墙”这一具体建材上。影壁是传统风水布局的重要元素，而“铝合金”材质则是现代工艺的体现。此行为表明用户已进入选材和技术细节对比阶段，实现了从宏观理念到微观部件的认知收敛。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-06-27】 + --:-- [直播-关注] <|living_begin|> +【2025-06-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-06】 + --:-- [直播-关注] <|living_begin|> +【2025-07-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-25】 + --:-- [直播-关注] <|living_begin|> +【2025-08-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-15】 + --:-- [直播-关注] <|living_begin|> +【2025-09-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-12】 + --:-- [直播-关注] <|living_begin|> +【2025-09-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-25】 + --:-- [直播-关注] <|living_begin|> +【2025-10-14】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-20】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-28】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + 20:10 [广告-深度转化] <|ad_begin|> +【2025-12-09】 + 08:26 [广告-深度转化] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 19:25 [广告-深度转化] <|ad_begin|> + 19:56 [广告-深度转化] <|ad_begin|> + 20:17 [广告-深度转化] <|ad_begin|> + 22:10 [广告-深度转化] <|ad_begin|> +【2025-12-12】 + 15:43 [广告-深度转化] <|ad_begin|> +【2025-12-14】 + 22:26 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + --:-- [直播-关注] <|living_begin|> +【2025-12-16】 + 19:42 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + 01:50 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 05:01 [广告-深度转化] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 09:35 [广告-深度转化] <|ad_begin|> + 12:45 [广告-深度转化] <|ad_begin|> + 15:24 [广告-深度转化] <|ad_begin|> + 15:54 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 06:14 [广告-深度转化] <|ad_begin|> + 07:43 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + 12:54 [广告-深度转化] <|ad_begin|> + 22:18 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + 18:19 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-30】 + 09:59 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 18:32 [广告-深度转化] <|ad_begin|> + 18:57 [广告-点击] <|ad_begin|> + 19:03 [广告-点击] <|ad_begin|> + 19:23 [广告-点击] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 09:16 [广告-深度转化] <|ad_begin|> + 09:16 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:59 [广告-点击] <|ad_begin|> + 22:04 [广告-深度转化] <|ad_begin|> + 22:04 [广告-点击] <|ad_begin|> +【2026-01-02】 + 04:03 [广告-点击] <|ad_begin|> + 04:05 [广告-点击] <|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|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-03】 + 10:16 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:12 [广告-深度转化] <|ad_begin|> + 22:12 [广告-点击] <|ad_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + 08:39 [广告-点击] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> + 17:30 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + 04:47 [广告-点击] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> + 11:47 [广告-点击] <|ad_begin|> + 19:39 [广告-深度转化] <|ad_begin|> + 19:39 [广告-点击] <|ad_begin|> +【2026-01-07】 + 09:20 [广告-深度转化] <|ad_begin|> + 09:20 [广告-点击] <|ad_begin|> + 23:04 [广告-点击] <|ad_begin|> +【2026-01-08】 + 04:41 [广告-深度转化] <|ad_begin|> + 04:41 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 18:47 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-首次打赏] <|living_begin|> + 04:51 [广告-深度转化] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 05:18 [广告-点击] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + 05:45 [广告-点击] <|ad_begin|> + 05:48 [广告-点击] <|ad_begin|> + 11:06 [广告-深度转化] <|ad_begin|> + 11:06 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:10 [广告-深度转化] <|ad_begin|> + 22:10 [广告-点击] <|ad_begin|> +【2026-01-10】 + 14:29 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:12 [广告-深度转化] <|ad_begin|> + 21:12 [广告-点击] <|ad_begin|> + 21:13 [广告-点击] <|ad_begin|> + 21:28 [广告-点击] <|ad_begin|> + 21:31 [广告-点击] <|ad_begin|> + 21:34 [广告-点击] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + 02:22 [广告-点击] <|ad_begin|> + 04:27 [广告-深度转化] <|ad_begin|> + 04:27 [广告-点击] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 06:36 [广告-深度转化] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + 03:40 [广告-深度转化] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 13:58 [广告-点击] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 14:37 [广告-深度转化] <|ad_begin|> + 14:37 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + 10:49 [广告-点击] <|ad_begin|> + 16:34 [广告-深度转化] <|ad_begin|> + 16:34 [广告-点击] <|ad_begin|> + 16:52 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + 14:34 [广告-深度转化] <|ad_begin|> + 14:34 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> + 20:07 [广告-点击] <|ad_begin|> + 20:14 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> +【2026-01-19】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + 17:38 [广告-深度转化] <|ad_begin|> + 17:38 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 17:13 [视频-关注/长播] <|video_begin|> + 17:29 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-23】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-24】 + 09:54 [视频-关注/收藏/点赞/长播] <|video_begin|> + 11:58 [视频-关注/点赞/长播] <|video_begin|> + 12:21 [广告-点击] <|ad_begin|> + 12:38 [广告-深度转化] <|ad_begin|> + 12:38 [广告-点击] <|ad_begin|> + 13:22 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-25】 + 10:22 [视频-点赞/长播] <|video_begin|> + 10:34 [广告-深度转化] <|ad_begin|> + 10:34 [广告-点击] <|ad_begin|> + 15:52 [广告-点击] <|ad_begin|> + 15:56 [广告-点击] <|ad_begin|> + 16:12 [视频-收藏/长播] <|video_begin|> + 16:27 [广告-点击] <|ad_begin|> +【2026-01-26】 + 10:35 [视频-关注/点赞/长播] <|video_begin|> + 10:40 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:54 [广告-深度转化] <|ad_begin|> + 17:54 [广告-点击] <|ad_begin|> + 18:09 [广告-深度转化] <|ad_begin|> + 18:09 [广告-点击] <|ad_begin|> +【2026-01-28】 + 22:22 [广告-点击] <|ad_begin|> + 22:37 [视频-关注/长播] <|video_begin|> + 22:46 [广告-点击] <|ad_begin|> +【2026-01-29】 + 05:30 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + 14:48 [广告-深度转化] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 21:29 [视频-长播] <|video_begin|> + 21:29 [视频-长播] <|video_begin|> + 21:30 [视频-长播] <|video_begin|> + 21:35 [视频-长播] <|video_begin|> + 21:35 [视频-长播] <|video_begin|> + 21:35 [视频-长播] <|video_begin|> + 21:35 [视频-长播] <|video_begin|> +【2026-01-31】 + 05:49 [视频-长播] <|video_begin|> + 05:57 [视频-长播] <|video_begin|> + 05:57 [视频-长播] <|video_begin|> + 05:57 [视频-长播] <|video_begin|> + 11:12 [广告-深度转化] <|ad_begin|> + 11:12 [广告-点击] <|ad_begin|> + 11:56 [视频-长播] <|video_begin|> + 12:05 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:32 [视频-长播] <|video_begin|> + 21:32 [视频-长播] <|video_begin|> + 21:52 [视频-长播] <|video_begin|> + 21:52 [视频-长播] <|video_begin|> + 21:52 [视频-长播] <|video_begin|> +【2026-02-01】 + 08:05 [视频-长播] <|video_begin|> + 08:35 [视频-长播] <|video_begin|> + 09:11 [视频-长播] <|video_begin|> + 10:18 [视频-长播] <|video_begin|> + 10:18 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-长播] <|video_begin|> + 17:18 [视频-长播] <|video_begin|> + 17:18 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:01 [广告-深度转化] <|ad_begin|> + 21:01 [广告-点击] <|ad_begin|> + 21:34 [视频-长播] <|video_begin|> + 21:47 [视频-长播] <|video_begin|> + 21:47 [视频-长播] <|video_begin|> + 21:51 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 03:15 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 05:09 [视频-长播] <|video_begin|> + 05:10 [视频-长播] <|video_begin|> + 05:10 [视频-长播] <|video_begin|> + 05:17 [广告-深度转化] <|ad_begin|> + 05:17 [广告-点击] <|ad_begin|> + 05:20 [视频-长播] <|video_begin|> + 05:20 [视频-长播] <|video_begin|> + 12:38 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 19:26 [视频-长播] <|video_begin|> + 19:28 [视频-长播] <|video_begin|> + 19:28 [视频-长播] <|video_begin|> + 19:28 [视频-关注/收藏/长播] <|video_begin|> + 19:40 [视频-转发/长播] <|video_begin|> + 20:03 [视频-长播] <|video_begin|> + 20:03 [视频-长播] <|video_begin|> + 20:03 [视频-长播] <|video_begin|> + 20:03 [视频-长播] <|video_begin|> + 20:03 [视频-长播] <|video_begin|> + 20:33 [广告-深度转化] <|ad_begin|> + 20:33 [广告-点击] <|ad_begin|> +【2026-02-03】 + 21:05 [视频-长播] <|video_begin|> + 21:35 [广告-深度转化] <|ad_begin|> + 21:35 [广告-点击] <|ad_begin|> + 21:48 [视频-长播] <|video_begin|> + 21:48 [视频-长播] <|video_begin|> + 22:00 [视频-长播] <|video_begin|> + 22:00 [视频-长播] <|video_begin|> +【2026-02-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-05】 + 09:42 [视频-评论/点赞/长播] <|video_begin|> + 10:07 [广告-点击] <|ad_begin|> + 10:26 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + --:-- [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心判定逻辑（递进性准则）： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 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 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：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请针对下列主题提取行为逻辑链： +主题：从被动种草到主动购买的兴趣确认与落地 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动种草到主动购买的兴趣确认与落地", + "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，我将根据任务要求，对用户的行为数据进行分析。 + +我的任务是分析用户的行为序列，找出一条从被动种草到主动购买的兴趣确认与落地路径。这意味着我需要识别出一个兴趣的萌芽点，然后观察这个兴趣是如何通过一系列行为逐步明确，并最终转化为具体购买决策的。 + +在梳理用户的整体行为时，我注意到用户在2026年1月之前，其消费和关注点主要集中在服装、家居日用品以及大量的短剧广告上，并没有表现出对植物或园艺的明显兴趣。 + +转折点出现在2026年1月11日，我观察到用户关注了一个专门销售杜鹃花并分享养护知识的直播间 <|living_begin|>。这个行为非常关键，因为它标志着用户首次对“花卉养护”这一特定领域产生了明确的、可被记录的兴趣。这位主播不仅是“露脸美女”，更重要的是其“花卉养护达人”和“专业干货分享”的标签，这表明用户可能是被专业的养护知识所吸引，而不仅仅是泛泛地浏览。这可以被看作是兴趣的“被动种草”阶段，用户的潜在需求被直播内容成功激活了。 + +紧接着，在三天后的1月14日，用户的兴趣迅速转化为实际行动。我看到了一笔购买记录，是购买了康乃馨 <|prod_begin|>。虽然购买的不是杜鹃花，但康乃馨作为一种常见且相对容易养护的开花植物，我推测这可能是用户在受到花卉直播启发后的一次初步尝试或“练手”。这次购买行为是兴趣从线上关注到线下实践的关键一步，验证了用户确实有种植花卉的意愿。 + +仅仅两天后，也就是1月16日，用户的行为进一步聚焦和深化。我发现用户直接购买了室内外盆栽的红色杜鹃花 <|prod_begin|>。这次购买行为与1月11日关注的直播间内容高度匹配，直接命中了“杜鹃花”这个核心品类。这表明，用户在经过初次购买康乃馨的尝试后，信心和兴趣得到了增强，最终决定回归并实践最初被种草的目标。这个行为清晰地展示了用户从一个宽泛的“想养花”的念头，经过一次试水，最终精准地落定到最初激发他兴趣的特定品类上，形成了一个完整的兴趣确认与购买闭环。 + +综合来看，我认为用户的行为路径清晰地展示了从兴趣被动触发到主动购买的完整过程。首先，通过关注杜鹃花专卖及养护科普的直播 <|living_begin|>，用户对花卉种植的兴趣被明确激活。随后，用户通过购买较为常见的康乃馨 <|prod_begin|> 进行初步尝试，将线上兴趣转化为线下实践。最终，在初步尝试后，用户的购买行为迅速聚焦到最初被种草的杜鹃花 <|prod_begin|>，完成了从泛兴趣到精准购买的转化。这一系列连贯的行为表明，用户未来可能对专业的花卉养护内容和相关商品有持续的消费潜力。 +{"logic_chain": {"name": "从被动种草到主动购买的兴趣确认与落地", "events": [{"date": "2026-01-11", "action": "[直播-关注] <|living_begin|>", "logic": "兴趣触发：通过关注专卖杜鹃花并进行养护科普的主播，用户对花卉种植的兴趣被明确激活。"}, {"date": "2026-01-14", "action": "[商品-购买] <|prod_begin|>", "logic": "首次行动转化：受直播内容影响，用户开始尝试购买花卉，选择了较为常见的康乃馨作为入门尝试。"}, {"date": "2026-01-16", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣聚焦与深化：在首次购买后，用户的行为迅速收敛至与主播核心领域（杜鹃花）完全一致的商品，完成了从泛兴趣到精准兴趣的落地，形成了完整的“种草-购买”闭环。"}]}}<|im_end|> +<|im_start|>user +你擅长把商品内容描述映射成精确的商品token。 +请根据以下商品描述生成匹配的商品token：这款厨房手动打蛋器专为烘焙与日常烹饪设计，采用304不锈钢材质，坚固耐用且易清洁。其半自动旋转结构，让使用者手持操作即可高效打发蛋液、奶油或搅拌油料，无需插电，省时省力。产品设计轻巧，握感舒适，适合家庭用户及烘焙爱好者。主图突出“比电动还快”与“拍一发二”的促销概念，强调其效率与高性价比。适用于打蛋、搅拌、混合等多种厨房场景，是厨房中不可或缺的实用小工具。售价仅5.37元，零运费，由平台自营保障，是追求便捷与实惠用户的理想选择。/no_think<|im_end|> +<|im_start|>assistant + + +<|prod_begin|><|im_end|> +<|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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+然而，一个关键的兴趣转变信号出现在2025年12月29日。在连续打赏了多位才艺型主播后，用户首次打赏了一个非常特别的直播间 <|living_begin|>。我注意到这个直播间的标签是“不露脸直播”、“快手极速版活跃教学”和“金币赚取攻略”。这与他之前的所有直播偏好都截然不同。这次打赏行为让我推测，用户的动机发生了显著变化。他不再仅仅满足于被动地观看和消费内容，而是开始主动探索平台本身的玩法机制，特别是那些与“赚钱”、“获利”相关的攻略。这标志着他从一个纯粹的娱乐消费者，开始向一个平台玩法探索者转变，认知上实现了从“花钱娱乐”到“研究如何赚钱”的跃迁。 + +为了验证这个推测，我继续向后追溯他的行为。果然，在2026年1月8日，我发现用户再次打赏了一个同类型的直播间 <|living_begin|>。这个直播间同样是“不露脸”的，核心内容是“快手极速版干货分享”、“签到打卡教学”和“金币攻略科普”。这次重复的打赏行为有力地印证了我的判断。这表明他对平台变现机制的兴趣并非一时兴起，而是已经形成了一个持续、稳定的关注点。他正在主动、深入地学习如何通过平台的各种任务和技巧来获取收益。 + +综合以上分析，我看到了一条清晰的认知演化路径。用户最初被直播的娱乐属性吸引，进行消费和打赏。但在这个过程中，他逐渐对平台内部的激励和变现机制产生了好奇，并开始主动学习和探索如何从中获利。这种从被动娱乐到主动探索变现的转变，是用户在平台生态中认知深化的重要体现。因此，我倾向于认为，后续可以向他推荐更多关于平台玩法、任务攻略或轻量级变现技巧的内容，有较大概率会吸引他的关注。 + +根据我的分析，我将这条行为逻辑链总结如下： +用户的行为起点是典型的娱乐消费，如在2025年12月15日打赏高颜值才艺型主播 <|living_begin|>。随后，在2025年12月29日，他首次打赏了一个教授“金币赚取攻略”和“账号权重科普”的教学型直播 <|living_begin|>，这标志着他的认知从纯粹的娱乐消费转向对平台变现机制的探索。最后，在2026年1月8日，他再次打赏了同类型的“快手极速版干货分享”直播 <|living_begin|>，进一步学习“停留互动技巧”，这表明他对该领域的兴趣得到了确认并持续深化。 +{"logic_chain": {"name": "从娱乐消遣到变现探索的认知演化", "events": [{"date": "2025-12-15", "action": "[直播-首次打赏] <|living_begin|>", "logic": "初始阶段：用户行为以典型的娱乐消费为主，关注并打赏高颜值、才艺型女主播，属于被动内容消费。"}, {"date": "2025-12-29", "action": "[直播-首次打赏] <|living_begin|>", "logic": "认知跃迁：用户首次打赏一个“不露脸”的教学型直播，其核心内容是“金币赚取攻略”和“账号权重科普”。这标志着用户从纯粹的观众，转变为对平台玩法和变现机制产生兴趣的探索者，实现了从“消费”到“生产/获利”的认知转变。"}, {"date": "2026-01-08", "action": "[直播-首次打赏] <|living_begin|>", "logic": "行为强化与深化：用户再次打赏同类型的“快手极速版干货分享”直播，进一步关注“停留互动技巧”等更深层次的玩法。这表明其对该领域的兴趣得到确认并持续深化，正在主动学习如何更有效地参与平台活动。"}]}}<|im_end|> + +好的，我来分析一下这项推荐数据挖掘任务。我的任务是基于用户的交互历史，挖掘出一条从被动获取信息到主动进行厨具升级决策的行为路径。 + +我首先梳理了用户的整体行为时间线，发现他在2026年1月22日这天的行为序列特别值得关注。在此之前，用户的消费和直播偏好主要集中在服饰、零食以及观看美女、才艺型主播上，与厨房用品的关联度并不高。然而，在1月22日，他的行为出现了一个明显的兴趣焦点转移。 + +当天，我注意到他观看了一个关于多品牌菜刀测评的视频 <|video_begin|>。这个视频详细对比了张小泉等多个品牌刀具的性能，这很可能首次激发了他对厨房工具专业性的认知。通常，这类测评内容会引发用户对自己现有工具的审视。紧接着，一个非常有趣的决策发生了：他并没有立刻购买一把新刀，而是购买了一件与视频中提到的品牌“张小泉”相关的商品——一个多功能磨刀器 <|prod_begin|>。我推测，这是他在接收到新信息后，采取的一种低成本、高效率的解决方案，试图先通过维护来提升现有刀具的性能，这是一种非常务实的行为。 + +这个行为链并没有就此中断。在解决了刀具锋利度这个基础问题后，我发现他的兴趣立刻发生了延伸和升级。同一天，他又观看了一个关于多品牌破壁机的视频 <|video_begin|>。这个视频展示了破壁机在制作健康饮品方面的便捷性和多功能性。从关注单一功能的切割工具（菜刀），到关注复合功能的厨房电器（破壁机），这在我看来是一次显著的认知跃迁。他不再仅仅满足于基础的烹饪准备工作，而是开始追求更高层次的、便捷化的健康生活方式，比如制作营养早餐。这个兴趣的迁移路径非常清晰，从一个具体问题的解决，自然地过渡到了对整个厨房生活品质提升的向往。 + +综合来看，我认为用户的行为展现了一个从被动信息接收到主动决策的完整闭环。他先是通过观看菜刀测评视频 <|video_begin|> 意识到厨具性能的重要性，随即购买了张小泉磨刀器 <|prod_begin|> 来优化现有工具，紧接着兴趣点又被另一个视频 <|video_begin|> 引导至功能更强大的破壁机上。这一系列行为逻辑紧密，层层递进，清晰地勾勒出他对于厨具升级的思考路径。因此，未来向他推荐高品质、多功能的厨房小家电，可能会获得不错的反馈。 +{"logic_chain": {"name": "从被动获取信息到主动购买决策的厨具升级路径", "events": [{"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "问题触发与知识获取：通过观看多品牌菜刀测评视频，触发了对厨房刀具性能的关注和认知升级的需求。"}, {"date": "2026-01-22", "action": "[商品-购买] <|prod_begin|>", "logic": "行为修正与实践：基于对刀具锋利度的认知，用户没有直接购买新刀，而是选择购买与视频中品牌“张小泉”相关的辅助工具——磨刀器，作为提升现有工具性能的低成本解决方案。"}, {"date": "2026-01-22", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣延伸与认知跃迁：在解决了刀具问题后，兴趣从单一的切割工具延伸到功能更复杂的厨房电器（破壁机），体现了从基础工具维护到追求更高生活品质（便捷健康早餐）的认知跃迁。"}]}}<|im_end|> + +好的，我将以推荐分析师的视角，对这位用户的行为数据进行解读。 + +我的任务是基于用户的交互历史，挖掘出一条从传统建筑风水兴趣到现代自建房设计探索的行为逻辑链。 + +在分析这份长达一年的交互记录时，我首先注意到用户在2025年展现出非常务实的生活消费偏好。他大量购买了食品、农产品、五金工具以及一些农机配件，例如防冻液<|prod_begin|>、钢丝钳<|prod_begin|>、播种机配件<|prod_begin|>和开垦刀<|prod_begin|>。同时，他关注的直播内容也多为接地气的草根歌手、时政分析和农机讲解<|living_begin|>，这些都描绘出一个关注实际生活、可能从事或熟悉农业/体力劳动的用户画像。 + +然而，进入2026年1月下旬，我观察到用户行为出现了一个非常有趣的转变。在1月28日，他浏览了一个关于北方建房风水理念的视频<|video_begin|>。这个视频探讨了如何利用“子孙道”等传统风水概念来优化空间布局。这对我来说是一个关键信号，表明用户可能产生了建房或改造房屋的初步想法，并且他的兴趣起点并非现代建筑设计，而是根植于传统文化和风水观念。 + +这个兴趣点在接下来的一周内迅速发酵并具体化。到了2月5日，我看到他密集地观看了一系列与建房相关的视频。其中一个视频<|video_begin|>展示了中式传统风格的乡村自建房模型，这恰好承接了他之前对风水理念的关注，将抽象的布局概念与具象的“中式传统建筑”实体联系了起来。这说明他的想法正在从“怎么建”的理论层面，发展到“建成什么样”的视觉化阶段。 + +紧接着，就在同一天，他的关注点进一步下沉到了具体的建筑构件和材料上。他观看了一个详细介绍铝合金影壁墙的视频<|video_begin|>。影壁本身就是中式建筑和风水布局中的一个标志性元素，具有保护隐私、挡风聚气的功能。而他关注的材质是“铝合金”，这是一种现代工业材料。这个行为非常有价值，因为它清晰地展示了用户正在试图将传统建筑元素（影壁）与现代工艺技术（铝合金）相结合，寻求一个兼顾传统美学与现代实用性的解决方案。这标志着他的探索已经从宏观的风格和理念，收敛到了具体部件的技术选型和落地实施层面。 + +综合来看，我认为用户的行为展现了一条清晰的递进逻辑。他从对传统建房风水的抽象兴趣出发，逐步将这种兴趣具象化为对中式乡村自建房模型的关注，最终聚焦到如何用现代材料实现传统建筑元素这一具体问题上。 + +因此，我推断用户的核心需求演变路径如下： +最初，用户在2026年1月28日因浏览关于北方建房风水理念和空间布局的视频<|video_begin|>，触发了建房的初步兴趣。随后在2月5日，这一兴趣变得更加具体，他开始观看中式传统建筑风格的乡村自建房模型视频<|video_begin|>，将抽象的风水概念与实际的建筑形态联系起来。最终，他的需求进一步细化，聚焦于具体的建筑构件和现代工艺，通过观看关于铝合金影壁墙设计与安装的视频<|video_begin|>，探索如何将传统元素与现代材料结合，进入了实际选材和技术考量的阶段。基于此，未来向他推荐融合传统设计与现代建材的自建房方案、相关施工工艺或建材品牌，有较大概率被他关注。 +{"logic_chain": {"name": "从传统建筑风水到现代自建房设计的落地探索", "events": [{"date": "2026-01-28", "action": "[视频-浏览] <|video_begin|>", "logic": "初始触发：用户对“建房风水理念”产生兴趣，关注如何通过传统理念优化“空间布局”，这是建房需求的抽象起点。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知具象化：用户的兴趣从抽象的风水概念，转变为对“中式传统建筑风格”的“乡村自建房屋模型”的具象化探索，开始将理念与实际建筑形态相结合。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "决策收敛与技术聚焦：用户进一步将注意力聚焦到“铝合金影壁墙”这一具体建材上。影壁是传统风水布局的重要元素，而“铝合金”材质则是现代工艺的体现。此行为表明用户已进入选材和技术细节对比阶段，实现了从宏观理念到微观部件的认知收敛。"}]}}<|im_end|> + +好的，我将根据任务要求，对用户的行为数据进行分析。 + +我的任务是分析用户的行为序列，找出一条从被动种草到主动购买的兴趣确认与落地路径。这意味着我需要识别出一个兴趣的萌芽点，然后观察这个兴趣是如何通过一系列行为逐步明确，并最终转化为具体购买决策的。 + +在梳理用户的整体行为时，我注意到用户在2026年1月之前，其消费和关注点主要集中在服装、家居日用品以及大量的短剧广告上，并没有表现出对植物或园艺的明显兴趣。 + +转折点出现在2026年1月11日，我观察到用户关注了一个专门销售杜鹃花并分享养护知识的直播间 <|living_begin|>。这个行为非常关键，因为它标志着用户首次对“花卉养护”这一特定领域产生了明确的、可被记录的兴趣。这位主播不仅是“露脸美女”，更重要的是其“花卉养护达人”和“专业干货分享”的标签，这表明用户可能是被专业的养护知识所吸引，而不仅仅是泛泛地浏览。这可以被看作是兴趣的“被动种草”阶段，用户的潜在需求被直播内容成功激活了。 + +紧接着，在三天后的1月14日，用户的兴趣迅速转化为实际行动。我看到了一笔购买记录，是购买了康乃馨 <|prod_begin|>。虽然购买的不是杜鹃花，但康乃馨作为一种常见且相对容易养护的开花植物，我推测这可能是用户在受到花卉直播启发后的一次初步尝试或“练手”。这次购买行为是兴趣从线上关注到线下实践的关键一步，验证了用户确实有种植花卉的意愿。 + +仅仅两天后，也就是1月16日，用户的行为进一步聚焦和深化。我发现用户直接购买了室内外盆栽的红色杜鹃花 <|prod_begin|>。这次购买行为与1月11日关注的直播间内容高度匹配，直接命中了“杜鹃花”这个核心品类。这表明，用户在经过初次购买康乃馨的尝试后，信心和兴趣得到了增强，最终决定回归并实践最初被种草的目标。这个行为清晰地展示了用户从一个宽泛的“想养花”的念头，经过一次试水，最终精准地落定到最初激发他兴趣的特定品类上，形成了一个完整的兴趣确认与购买闭环。 + +综合来看，我认为用户的行为路径清晰地展示了从兴趣被动触发到主动购买的完整过程。首先，通过关注杜鹃花专卖及养护科普的直播 <|living_begin|>，用户对花卉种植的兴趣被明确激活。随后，用户通过购买较为常见的康乃馨 <|prod_begin|> 进行初步尝试，将线上兴趣转化为线下实践。最终，在初步尝试后，用户的购买行为迅速聚焦到最初被种草的杜鹃花 <|prod_begin|>，完成了从泛兴趣到精准购买的转化。这一系列连贯的行为表明，用户未来可能对专业的花卉养护内容和相关商品有持续的消费潜力。 +{"logic_chain": {"name": "从被动种草到主动购买的兴趣确认与落地", "events": [{"date": "2026-01-11", "action": "[直播-关注] <|living_begin|>", "logic": "兴趣触发：通过关注专卖杜鹃花并进行养护科普的主播，用户对花卉种植的兴趣被明确激活。"}, {"date": "2026-01-14", "action": "[商品-购买] <|prod_begin|>", "logic": "首次行动转化：受直播内容影响，用户开始尝试购买花卉，选择了较为常见的康乃馨作为入门尝试。"}, {"date": "2026-01-16", "action": "[商品-购买] <|prod_begin|>", "logic": "兴趣聚焦与深化：在首次购买后，用户的行为迅速收敛至与主播核心领域（杜鹃花）完全一致的商品，完成了从泛兴趣到精准兴趣的落地，形成了完整的“种草-购买”闭环。"}]}}<|im_end|> + + +<|prod_begin|><|im_end|> +[INFO|configuration_utils.py:771] 2026-07-06 02:31:59,593 >> 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 02:31:59,594 >> 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 02:31:59] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-06 02:31:59] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-06 02:32:00,344 >> 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 02:32:00,344 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-06 02:32:00,394 >> 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 02:32:01,252 >> 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 02:32:01,564 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-06 02:32:01] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-06 02:32:01] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-06 02:32:01] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-06 02:32:01] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-06 02:32:01] 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 02:32:01,591 >> 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 02:32:01,697 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-06 02:32:01,697 >> Num examples = 2,923 +[INFO|trainer.py:1471] 2026-07-06 02:32:01,697 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-06 02:32:01,697 >> Num update steps per epoch = 731 +[INFO|trainer.py:1473] 2026-07-06 02:32:01,697 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-06 02:32:01,697 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-06 02:32:01,697 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-06 02:32:01,697 >> Total optimization steps = 731 +[INFO|trainer.py:1479] 2026-07-06 02:32:01,697 >> Number of trainable parameters = 801,433,600 + 0%| | 0/731 [00:00> `use_return_dict` is deprecated! 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Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-500 +[INFO|configuration_utils.py:533] 2026-07-06 03:43:36,455 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-500/config.json +[INFO|configuration_utils.py:816] 2026-07-06 03:43:36,455 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-500/generation_config.json + +{'loss': '1.426', 'grad_norm': '2.734', 'learning_rate': '6.015e-06', 'epoch': '0.6432'} +{'loss': '1.412', 'grad_norm': '3.031', 'learning_rate': '5.813e-06', 'epoch': '0.65'} +{'loss': '1.469', 'grad_norm': '2.25', 'learning_rate': '5.613e-06', 'epoch': '0.6569'} +{'loss': '1.364', 'grad_norm': '2.875', 'learning_rate': '5.415e-06', 'epoch': '0.6637'} +{'loss': '1.37', 'grad_norm': '2.547', 'learning_rate': '5.219e-06', 'epoch': '0.6705'} +{'loss': '1.322', 'grad_norm': 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/home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-731/generation_config.json +{'loss': '1.335', 'grad_norm': '3.266', 'learning_rate': '4.646e-06', 'epoch': '0.6911'} +{'loss': '1.357', 'grad_norm': '3.531', 'learning_rate': '4.46e-06', 'epoch': '0.6979'} +{'loss': '1.363', 'grad_norm': '3.547', 'learning_rate': '4.277e-06', 'epoch': '0.7048'} +{'loss': '1.416', 'grad_norm': '2.5', 'learning_rate': '4.097e-06', 'epoch': '0.7116'} +{'loss': '1.43', 'grad_norm': '2.641', 'learning_rate': '3.92e-06', 'epoch': '0.7184'} +{'loss': '1.37', 'grad_norm': '2.375', 'learning_rate': '3.745e-06', 'epoch': '0.7253'} +{'loss': '1.402', 'grad_norm': '2.484', 'learning_rate': '3.574e-06', 'epoch': '0.7321'} +{'loss': '1.41', 'grad_norm': '3.156', 'learning_rate': '3.406e-06', 'epoch': '0.739'} +{'loss': '1.403', 'grad_norm': '2.234', 'learning_rate': '3.241e-06', 'epoch': '0.7458'} +{'loss': '1.429', 'grad_norm': '2.344', 'learning_rate': '3.079e-06', 'epoch': '0.7527'} +{'loss': '1.406', 'grad_norm': '2.484', 'learning_rate': '2.921e-06', 'epoch': '0.7595'} +{'loss': '1.352', 'grad_norm': '3', 'learning_rate': '2.766e-06', 'epoch': '0.7663'} +{'loss': '1.398', 'grad_norm': '3.781', 'learning_rate': '2.615e-06', 'epoch': '0.7732'} +{'loss': '1.402', 'grad_norm': '2.594', 'learning_rate': '2.468e-06', 'epoch': '0.78'} +{'loss': '1.496', 'grad_norm': '2.062', 'learning_rate': '2.324e-06', 'epoch': '0.7869'} +{'loss': '1.452', 'grad_norm': '2.781', 'learning_rate': '2.184e-06', 'epoch': '0.7937'} +{'loss': '1.379', 'grad_norm': '2.203', 'learning_rate': '2.047e-06', 'epoch': '0.8005'} +{'loss': '1.411', 'grad_norm': '2.438', 'learning_rate': '1.915e-06', 'epoch': '0.8074'} +{'loss': '1.345', 'grad_norm': '3.688', 'learning_rate': '1.787e-06', 'epoch': '0.8142'} +{'loss': '1.451', 'grad_norm': '2.953', 'learning_rate': '1.662e-06', 'epoch': '0.8211'} +{'loss': '1.335', 'grad_norm': '2.641', 'learning_rate': '1.542e-06', 'epoch': '0.8279'} +{'loss': '1.256', 'grad_norm': '2.578', 'learning_rate': '1.426e-06', 'epoch': '0.8348'} +{'loss': '1.355', 'grad_norm': '3.75', 'learning_rate': '1.314e-06', 'epoch': '0.8416'} +{'loss': '1.397', 'grad_norm': '2.609', 'learning_rate': '1.206e-06', 'epoch': '0.8484'} +{'loss': '1.348', 'grad_norm': '2.766', 'learning_rate': '1.103e-06', 'epoch': '0.8553'} +{'loss': '1.371', 'grad_norm': '2.516', 'learning_rate': '1.004e-06', 'epoch': '0.8621'} +{'loss': '1.373', 'grad_norm': '2.359', 'learning_rate': '9.095e-07', 'epoch': '0.869'} +{'loss': '1.362', 'grad_norm': '2.453', 'learning_rate': '8.195e-07', 'epoch': '0.8758'} +{'loss': '1.44', 'grad_norm': '2.25', 'learning_rate': '7.339e-07', 'epoch': '0.8827'} +{'loss': '1.392', 'grad_norm': '2.969', 'learning_rate': '6.529e-07', 'epoch': '0.8895'} +{'loss': '1.327', 'grad_norm': '4.219', 'learning_rate': '5.764e-07', 'epoch': '0.8963'} +{'loss': '1.343', 'grad_norm': '2.828', 'learning_rate': '5.046e-07', 'epoch': '0.9032'} +{'loss': '1.438', 'grad_norm': '1.977', 'learning_rate': '4.375e-07', 'epoch': '0.91'} +{'loss': '1.34', 'grad_norm': '2.359', 'learning_rate': '3.75e-07', 'epoch': '0.9169'} +{'loss': '1.45', 'grad_norm': '2.328', 'learning_rate': '3.173e-07', 'epoch': '0.9237'} +{'loss': '1.381', 'grad_norm': '4.219', 'learning_rate': '2.643e-07', 'epoch': '0.9306'} +{'loss': '1.306', 'grad_norm': '2.781', 'learning_rate': '2.161e-07', 'epoch': '0.9374'} +{'loss': '1.468', 'grad_norm': '2.281', 'learning_rate': '1.727e-07', 'epoch': '0.9442'} +{'loss': '1.392', 'grad_norm': '2.422', 'learning_rate': '1.341e-07', 'epoch': '0.9511'} +{'loss': '1.436', 'grad_norm': '2.312', 'learning_rate': '1.004e-07', 'epoch': '0.9579'} +{'loss': '1.453', 'grad_norm': '2.578', 'learning_rate': '7.148e-08', 'epoch': '0.9648'} +{'loss': '1.341', 'grad_norm': '2.094', 'learning_rate': '4.748e-08', 'epoch': '0.9716'} +{'loss': '1.377', 'grad_norm': '2.516', 'learning_rate': '2.836e-08', 'epoch': '0.9784'} +{'loss': '1.33', 'grad_norm': '3.359', 'learning_rate': '1.413e-08', 'epoch': '0.9853'} +{'loss': '1.295', 'grad_norm': '2.234', 'learning_rate': '4.81e-09', 'epoch': '0.9921'} +{'loss': '1.357', 'grad_norm': '3.359', 'learning_rate': '3.927e-10', 'epoch': '0.999'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-731/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 04:16:50,043 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-731/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 04:16:50,063 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/checkpoint-731/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-06 04:16:52,121 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 731/731 [1:44:50<00:00, 8.09s/it] 100%|██████████| 731/731 [1:44:50<00:00, 8.61s/it] +[INFO|trainer.py:3815] 2026-07-06 04:16:52,122 >> Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5 +[INFO|configuration_utils.py:533] 2026-07-06 04:16:52,123 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/config.json +[INFO|configuration_utils.py:816] 2026-07-06 04:16:52,124 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/generation_config.json +{'train_runtime': '6290', 'train_samples_per_second': '0.465', 'train_steps_per_second': '0.116', 'train_loss': '1.473', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 04:16:52,919 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 04:16:52,940 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 328584250GF + train_loss = 1.4734 + train_runtime = 1:44:50.42 + train_samples_per_second = 0.465 + train_steps_per_second = 0.116 +Figure saved at: /home/ll/llm4rec/experiments/20260706_overnight/outputs/v05_user_item_up_lr2e5/training_loss.png +[WARNING|2026-07-06 04:16:53] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-06 04:16:53] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-06 04:16:53,044 >> 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..4b71957 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.5281465280347546e+17, + "train_loss": 1.4733744319910553, + "train_runtime": 6290.4242, + "train_samples_per_second": 0.465, + "train_steps_per_second": 0.116 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..f63d95c --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,147 @@ +{"current_steps": 5, "total_steps": 731, "loss": 2.809466743469238, "lr": 3.6363636363636366e-06, "epoch": 0.006842285323297981, "percentage": 0.68, "elapsed_time": "0:00:42", "remaining_time": "1:42:44"} +{"current_steps": 10, "total_steps": 731, "loss": 2.548305892944336, "lr": 8.181818181818183e-06, "epoch": 0.013684570646595963, "percentage": 1.37, "elapsed_time": 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