From 6ba3c93ef6800ad3418ddad90baadaaa35acf33d Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Tue, 21 Jul 2026 11:17:10 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/v52_public091_full_lr2e5_ep1 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 77 + generation_config.json | 12 + model.safetensors | 3 + ...23678.ubuntu-System-Product-Name.1430897.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2252 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 33 + trainer_state.json | 267 ++ training_args.bin | 3 + training_loss.png | Bin 0 -> 35746 bytes 17 files changed, 2931 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 experiment_recipe.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul17_01-41-18_ubuntu-System-Product-Name/events.out.tfevents.1784223678.ubuntu-System-Product-Name.1430897.0 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 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+*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..2618634 --- /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: v52_public091_full_lr2e5_ep1 + results: [] +--- + + + +# v52_public091_full_lr2e5_ep1 + +This model is a fine-tuned version of [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the exp_v49_public_091_exact 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: 202607202 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 1.0 + +### Training results + + + +### Framework versions + +- Transformers 5.6.0 +- Pytorch 2.7.1+cu126 +- Datasets 4.0.0 +- Tokenizers 0.22.2 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..57cd2d4 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.563295846805975e+17, + "train_loss": 1.6355595296145948, + "train_runtime": 2594.717, + "train_samples_per_second": 0.503, + "train_steps_per_second": 0.126 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..8e2b786 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,77 @@ +{ + "created_at": "2026-07-16 23:52:50", + "name": "v52_public091_full_lr2e5_ep1", + "base": "OpenOneRec/OneReason-0.8B-pretrain-competition", + "dataset": "v49_public_091_exact", + "dataset_manifest": { + "path": "/home/ll/llm4rec/experiments/data/v49_public_091_exact.jsonl", + "records": 32705, + "groups": { + "rec": 18651, + "item": 9684, + "user": 2792, + "world": 1578 + }, + "routes": { + "no_think": 20934, + "think": 11771 + }, + "thought_modes": { + "empty": 20934, + "filled": 11771 + }, + "sha256": "cdedea13c560d3453697f4a3c9b96a2303bd708c84833034fa953d16482b9925", + "source_path": "/home/ll/llm4rec/data/external/kuaishou-llmrec-sft-baseline-0.91/train.jsonl", + "source_url": "https://huggingface.co/datasets/Frinkleko/kuaishou-llmrec-sft-baseline-0.91/resolve/main/train.jsonl?download=true", + "source_sha256": "4d6b29d76974c9a1517c1b583858e744cae019cb26e1e2d90066e237ebbcf5f8", + "source_license": "Apache-2.0", + "transform": "Lossless field mapping from system/prompt/response to instruction/input/output; original seed-42 order retained." + }, + "learning_rate": "2.0e-5", + "epochs": 1.0, + "method": "full", + "weight_decay": 0.0, + "validation_size": 0.0, + "lora": { + "rank": null, + "alpha": null, + "dropout": null, + "target": null + }, + "stages": null, + "note": "Full-SFT counterpart using v7's successful 2e-5 scale, now on balanced CoT/direct clean data instead of final-only data.", + "evaluation": null, + "cot_policy": "Preserve meaningful CoT for /think and pure final outputs for /no_think.", + "checkpoint_policy": "save_strategy=no; only the final model is retained.", + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "bc57a4d1337c7182996ece63ddc844185043b68ae2a2dbe793e3969bbccc2e2d", + "data_builder": "/home/ll/llm4rec/experiments/source_data.py", + "data_builder_sha256": "a1216b14c8c516f9fde2fcaab9d70ec5b8a200033fbd1ee330c6b461213587ad", + "data_cleaner": null, + "data_cleaner_sha256": null, + "configs": [ + "/home/ll/llm4rec/experiments/configs/v52_public091_full_lr2e5_ep1.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_experiments.py --single v52_public091_full_lr2e5_ep1 --gpu ", + "training_result": { + "name": "v52_public091_full_lr2e5_ep1", + "gpu": 1, + "started_at": "2026-07-17 01:41:03", + "status": "ok", + "wall_seconds": 2611.22, + "finished_at": "2026-07-17 02:24:34", + "epoch": 1.0, + "total_flos": 1.563295846805975e+17, + "train_loss": 1.6355595296145948, + "train_runtime": 2594.717, + "train_samples_per_second": 0.503, + "train_steps_per_second": 0.126, + "global_step": 326, + "first_logged_loss": 2.659077453613281, + "last_logged_loss": 1.512490463256836, + "min_logged_loss": 1.5008227348327636, + "tail_loss_mean": 1.5448276201883953, + "logged_loss_change": -1.1465869903564452, + "batch": "v49-v53" + } +} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..6f93b96 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8f026ea380644a0869b10aee9df9288534d15a1ca9e1b2f9b25383de60db9ed +size 1602902832 diff --git a/runs/Jul17_01-41-18_ubuntu-System-Product-Name/events.out.tfevents.1784223678.ubuntu-System-Product-Name.1430897.0 b/runs/Jul17_01-41-18_ubuntu-System-Product-Name/events.out.tfevents.1784223678.ubuntu-System-Product-Name.1430897.0 new file mode 100644 index 0000000..1cc2c63 --- /dev/null +++ b/runs/Jul17_01-41-18_ubuntu-System-Product-Name/events.out.tfevents.1784223678.ubuntu-System-Product-Name.1430897.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f55f931403f23c73e55a1a6df47f51344a55af18b8882d66fa768d6967f35822 +size 12998 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..9b55322 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": false, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..afc3773 --- /dev/null +++ b/train.log @@ -0,0 +1,2252 @@ +[2026-07-17 01:41:03] stage 1/1: /home/ll/llm4rec/experiments/configs/v52_public091_full_lr2e5_ep1.yaml +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-17 01:41:06] 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] 2026-07-17 01:41:06,872 >> 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] 2026-07-17 01:41:06,874 >> 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] 2026-07-17 01:41:11,231 >> 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] 2026-07-17 01:41:11,232 >> 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] 2026-07-17 01:41:11,699 >> 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] 2026-07-17 01:41:11,700 >> 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|2026-07-17 01:41:13] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v49_public_091_exact.jsonl... +training example: +input_ids: +[151644, 872, 198, 10904, 20002, 108221, 100022, 10958, 28311, 10904, 17, 15, 17, 18, 12, 15, 21, 12, 16, 21, 10958, 198, 220, 1177, 25, 313, 508, 45943, 12, 103946, 60, 220, 176247, 156770, 160182, 170721, 198, 10904, 17, 15, 17, 18, 12, 15, 21, 12, 16, 22, 10958, 198, 220, 1177, 25, 313, 508, 45943, 12, 103946, 60, 220, 176247, 153149, 163664, 173075, 198, 10904, 17, 15, 17, 18, 12, 15, 21, 12, 16, 23, 10958, 198, 220, 1177, 25, 313, 508, 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113560, 3837, 100143, 112078, 55806, 71134, 3837, 20412, 104082, 105813, 101076, 9370, 115690, 1773, 151645, 198, 151643] +inputs: +<|im_start|>user +【用户交互历史】： +【2023-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2023-06-17】 + --:-- [商品-购买] <|prod_begin|> +【2023-06-18】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-15】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-18】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2023-08-23】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-13】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-15】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-28】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [直播-关注] <|living_begin|> +【2025-01-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-13】 + --:-- [直播-关注] <|living_begin|> +【2025-03-13】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [直播-关注] <|living_begin|> +【2025-04-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-09】 + --:-- [直播-关注] <|living_begin|> +【2025-05-16】 + --:-- [直播-关注] <|living_begin|> +【2025-05-26】 + --:-- [直播-关注] <|living_begin|> +【2025-05-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-29】 + --:-- [直播-关注] <|living_begin|> +【2025-06-04】 + --:-- [直播-关注] <|living_begin|> +【2025-06-14】 + --:-- [直播-关注] <|living_begin|> +【2025-06-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-09】 + --:-- [直播-关注] <|living_begin|> +【2025-07-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [直播-打赏] <|living_begin|> +【2025-08-10】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-13】 + --:-- [直播-关注] <|living_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-10-17】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-10-26】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> +【2025-11-15】 + --:-- [直播-关注] <|living_begin|> +【2025-11-18】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-26】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-28】 + --:-- [商品-点击] <|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|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-07】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-19】 + --:-- [商品-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|> +【2026-01-20】 + 06:53 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + 00:19 [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|> + 10:18 [广告-深度转化] <|ad_begin|> + 23:38 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 06:57 [视频-收藏/长播] <|video_begin|> + 11:39 [视频-收藏/长播] <|video_begin|> + 12:27 [广告-深度转化] <|ad_begin|> + 14:54 [广告-深度转化] <|ad_begin|> + 16:35 [广告-深度转化] <|ad_begin|> + 17:59 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + 03:44 [广告-深度转化] <|ad_begin|> + 08:10 [广告-深度转化] <|ad_begin|> + 08:38 [广告-深度转化] <|ad_begin|> + 10:42 [视频-收藏/长播] <|video_begin|> + 11:03 [视频-收藏/长播] <|video_begin|> + 23:20 [视频-收藏/长播] <|video_begin|> +【2026-01-24】 + 01:22 [广告-深度转化] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + 02:04 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:27 [广告-深度转化] <|ad_begin|> + 06:27 [广告-点击] <|ad_begin|> + 08:42 [广告-深度转化] <|ad_begin|> + 08:42 [广告-点击] <|ad_begin|> + 10:15 [广告-深度转化] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 11:18 [广告-深度转化] <|ad_begin|> + 11:18 [广告-点击] <|ad_begin|> + 14:23 [广告-深度转化] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 16:28 [广告-点击] <|ad_begin|> + 16:34 [视频-收藏/长播] <|video_begin|> + 17:21 [广告-深度转化] <|ad_begin|> + 17:21 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [商品-点击] <|prod_begin|> + 03:45 [广告-深度转化] <|ad_begin|> + 03:45 [广告-点击] <|ad_begin|> + 05:31 [广告-深度转化] <|ad_begin|> + 05:31 [广告-点击] <|ad_begin|> + 09:18 [广告-深度转化] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 13:27 [广告-深度转化] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> +【2026-01-26】 + 00:42 [视频-点赞/长播] <|video_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 02:56 [广告-点击] <|ad_begin|> + 04:11 [视频-评论/长播] <|video_begin|> + 06:48 [广告-深度转化] <|ad_begin|> + 06:48 [广告-点击] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 09:22 [广告-点击] <|ad_begin|> + 10:24 [视频-收藏/长播] <|video_begin|> + 11:23 [广告-深度转化] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 14:25 [广告-深度转化] <|ad_begin|> + 14:25 [广告-点击] <|ad_begin|> +【2026-01-27】 + 01:31 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:01 [广告-深度转化] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> + 11:01 [广告-深度转化] <|ad_begin|> + 11:01 [广告-点击] <|ad_begin|> + 14:21 [广告-深度转化] <|ad_begin|> + 14:21 [广告-点击] <|ad_begin|> + 15:23 [广告-深度转化] <|ad_begin|> + 15:23 [广告-点击] <|ad_begin|> + 23:58 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:50 [广告-深度转化] <|ad_begin|> + 04:50 [广告-点击] <|ad_begin|> + 07:44 [广告-深度转化] <|ad_begin|> + 07:44 [广告-点击] <|ad_begin|> + 12:20 [广告-深度转化] <|ad_begin|> + 12:20 [广告-点击] <|ad_begin|> + 13:15 [广告-深度转化] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> + 15:22 [广告-深度转化] <|ad_begin|> + 15:22 [广告-点击] <|ad_begin|> + 15:34 [视频-评论/长播] <|video_begin|> + 15:55 [广告-深度转化] <|ad_begin|> + 15:55 [广告-点击] <|ad_begin|> +【2026-01-29】 + 01:12 [广告-深度转化] <|ad_begin|> + 01:12 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:05 [广告-点击] <|ad_begin|> + 07:04 [广告-深度转化] <|ad_begin|> + 07:04 [广告-点击] <|ad_begin|> + 10:24 [视频-点赞/长播] <|video_begin|> + 11:08 [广告-深度转化] <|ad_begin|> + 11:08 [广告-点击] <|ad_begin|> + 13:42 [广告-深度转化] <|ad_begin|> + 13:42 [广告-点击] <|ad_begin|> + 14:50 [广告-深度转化] <|ad_begin|> + 14:50 [广告-点击] <|ad_begin|> + 15:43 [广告-深度转化] <|ad_begin|> + 15:43 [广告-点击] <|ad_begin|> +【2026-01-30】 + 01:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:22 [广告-深度转化] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 05:54 [广告-深度转化] <|ad_begin|> + 05:54 [广告-点击] <|ad_begin|> + 09:25 [广告-深度转化] <|ad_begin|> + 09:25 [广告-点击] <|ad_begin|> + 10:10 [广告-深度转化] <|ad_begin|> + 10:10 [广告-点击] <|ad_begin|> + 11:22 [广告-深度转化] <|ad_begin|> + 11:22 [广告-点击] <|ad_begin|> + 12:40 [广告-深度转化] <|ad_begin|> + 12:40 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 05:02 [广告-深度转化] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + 06:59 [广告-点击] <|ad_begin|> + 07:54 [广告-点击] <|ad_begin|> + 09:32 [视频-关注/长播] <|video_begin|> + 10:30 [广告-点击] <|ad_begin|> + 11:44 [广告-深度转化] <|ad_begin|> + 11:44 [广告-点击] <|ad_begin|> + 14:41 [广告-深度转化] <|ad_begin|> + 14:41 [广告-点击] <|ad_begin|> +【2026-02-01】 + 01:26 [广告-深度转化] <|ad_begin|> + 01:26 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:28 [广告-深度转化] <|ad_begin|> + 05:28 [广告-点击] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 10:03 [广告-点击] <|ad_begin|> + 13:41 [视频-浏览] <|video_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> + 14:57 [广告-深度转化] <|ad_begin|> + 14:57 [广告-点击] <|ad_begin|> +【2026-02-02】 + 00:13 [广告-深度转化] <|ad_begin|> + 00:13 [广告-点击] <|ad_begin|> + 05:27 [广告-深度转化] <|ad_begin|> + 05:27 [广告-点击] <|ad_begin|> + 08:13 [广告-深度转化] <|ad_begin|> + 08:13 [广告-点击] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> + 12:26 [广告-点击] <|ad_begin|> + 13:05 [广告-深度转化] <|ad_begin|> + 13:05 [广告-点击] <|ad_begin|> +【2026-02-03】 + 03:27 [广告-深度转化] <|ad_begin|> + 03:27 [广告-点击] <|ad_begin|> + 06:47 [广告-点击] <|ad_begin|> + 08:05 [视频-长播] <|video_begin|> + 08:05 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:17 [视频-长播] <|video_begin|> + 08:17 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:37 [视频-评论/长播] <|video_begin|> + 08:39 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:10 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:59 [广告-深度转化] <|ad_begin|> + 12:59 [广告-点击] <|ad_begin|> + 15:25 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> +【2026-02-04】 + 00:38 [视频-长播] <|video_begin|> + 00:38 [视频-长播] <|video_begin|> + 01:03 [广告-深度转化] <|ad_begin|> + 01:03 [广告-点击] <|ad_begin|> + 04:02 [视频-长播] <|video_begin|> + 04:04 [视频-评论/长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 05:17 [广告-深度转化] <|ad_begin|> + 05:17 [广告-点击] <|ad_begin|> + 08:45 [视频-长播] <|video_begin|> + 08:45 [视频-长播] <|video_begin|> + 08:45 [视频-长播] <|video_begin|> + 08:48 [视频-长播] <|video_begin|> + 08:48 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> + 08:57 [视频-长播] <|video_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 13:13 [视频-长播] <|video_begin|> + 13:27 [广告-深度转化] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> + 15:57 [视频-长播] <|video_begin|> + 16:19 [广告-深度转化] <|ad_begin|> + 16:19 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:36 [广告-深度转化] <|ad_begin|> + 00:36 [广告-点击] <|ad_begin|> + 03:23 [视频-长播] <|video_begin|> + 05:07 [视频-长播] <|video_begin|> + 05:19 [视频-长播] <|video_begin|> + 05:19 [视频-长播] <|video_begin|> + 05:19 [视频-长播] <|video_begin|> + 05:19 [视频-长播] <|video_begin|> + 05:28 [视频-长播] <|video_begin|> + 05:28 [视频-长播] <|video_begin|> + 05:35 [视频-长播] <|video_begin|> + 05:35 [视频-长播] <|video_begin|> + 05:35 [视频-长播] <|video_begin|> + 05:35 [视频-长播] <|video_begin|> + 05:35 [视频-评论/长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 06:02 [广告-深度转化] <|ad_begin|> + 06:02 [广告-点击] <|ad_begin|> + 10:30 [视频-长播] <|video_begin|> +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题提炼具有递进逻辑与内部关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 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 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请针对以下主题提取行为逻辑链： +主题：从被动信息流消费到主动健康问询的意识觉醒 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动信息流消费到主动健康问询的意识觉醒", + "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，这项任务要求我分析用户的行为时间线，挖掘出一条从被动娱乐消费到主动关注健康信息的行为逻辑链。我将重点关注用户兴趣的转变节点，并解释其背后的动机变化。 + +我首先注意到，这位用户的行为在很长一段时间内展现出非常稳定且广泛的偏好。从2023年到2025年初，他的购物记录非常务实且多样化，涵盖了食品如酸菜鱼<|prod_begin|>和鸡爪<|prod_begin|>，家居用品如拖把<|prod_begin|>和垃圾袋<|prod_begin|>，以及一些工具类的商品，比如水平仪<|prod_begin|>、扳手<|prod_begin|>和电钻配件<|prod_begin|>。这些消费行为描绘出一个注重生活实用性的中年男性形象。同时，他购买的保健品如黄瓜籽粉<|prod_begin|>、耳耵聍清洗器<|prod_begin|>和各类抑菌膏、草本片剂，也暗示了他对身体健康的潜在关注。 + +进入2025年后，我发现他的兴趣开始明显向直播内容倾斜，尤其偏爱东北地区的主播。他关注的直播间<|living_begin|>、<|living_begin|>、<|living_begin|>等，大多带有浓厚的东北地域特色、接地气的唠嗑风格和才艺表演。这表明他在寻求一种娱乐化、有陪伴感的互动体验。 + +然而，一个显著的兴趣迁移发生在2026年1月下旬。从1月20日开始，我观察到他陷入了对短剧广告的高强度、沉浸式消费中。在短短十几天里，他深度转化或点击了大量的短剧广告，例如《斩仙台真人AI版》<|ad_begin|>、《阴阳神算》<|ad_begin|>、《假太监：掠夺气运从祸乱皇朝开始》<|ad_begin|>等等。这些广告内容多以古装、仙侠、权谋、情感纠葛为主题，剧情紧凑、冲突强烈。这种行为模式非常典型，属于被动地被信息流中的爽文式、快节奏娱乐内容所吸引，并持续进行消费。 + +就在这种高频次的娱乐内容消费浪潮中，我捕捉到了一个关键的转折点。在连续多日沉浸于短剧广告之后，到1月31日，他的行为突然出现了一次性质上的剧变。他主动关注了一个以“中医健康科普”和“痛风知识讲解”为核心内容的直播间<|living_begin|>。这个直播间的主播是“长发医师”，穿着白大褂进行“公益免费问诊”，内容严肃且专业。这次交互与之前被动刷短剧的娱乐行为形成了鲜明对比，从消磨时间的娱乐需求，一跃成为带有明确目的性的主动健康问询。我推测，之前购买保健品的行为只是浅层的健康意识，而这次主动关注专业医师的科普直播，标志着他的健康意识被真正唤醒，开始从被动接收信息转向主动寻求专业解答。 + +综合来看，我认为用户的行为演变路径是清晰的。他最初的消费行为体现了对生活实用性和身体健康的朴素关注。随后，他沉浸在信息流推荐的短剧广告中，表现为一种被动的娱乐内容消费。最终，这种状态被一次关键的行为转变所打破，他开始主动关注并寻求专业的健康知识，完成了从被动娱乐到主动健康问询的意识觉醒。 + +因此，我提炼出如下行为逻辑链： +最初，用户长时间沉浸在信息流推荐的短剧广告中，例如在2026年1月20日深度转化了《斩仙台真人AI版》<|ad_begin|>，这代表了他被动、高频的娱乐内容消费习惯。然而，在经历了连续多日的娱乐内容消费后，用户在2026年1月31日首次主动关注了一个以“中医健康科普”和“痛风知识讲解”为主题的专业医师直播间<|living_begin|>，这标志着他的注意力从被动娱乐显著转移至主动的健康问询，完成了一次关键的需求认知升级。 +{"logic_chain": {"name": "从被动信息流消费到主动健康问询的意识觉醒", "events": [{"date": "2026-01-20", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始状态：用户沉浸在信息流推荐的短剧广告中，行为模式为被动、高频的娱乐内容消费。"}, {"date": "2026-01-31", "action": "[直播-关注] <|living_begin|>", "logic": "行为转折与认知跃迁：在连续多日的娱乐内容轰炸后，用户行为首次出现断崖式转变，从被动接收娱乐信息转向主动关注严肃的“中医健康科普”、“痛风知识讲解”等专业内容，交互深度和意图明确性显著增强，标志着一次重要的注意力转移和需求觉醒。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-07-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> +【2025-07-21】 + --:-- [直播-关注] <|living_begin|> +【2025-07-22】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-04】 + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-04】 + --:-- [直播-关注] <|living_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-01】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-28】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + 02:54 [广告-深度转化] <|ad_begin|> + 03:46 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 08:33 [广告-深度转化] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 14:53 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 10:01 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 00:29 [广告-深度转化] <|ad_begin|> + 00:40 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 09:50 [广告-深度转化] <|ad_begin|> + 11:58 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 11:44 [广告-深度转化] <|ad_begin|> + 12:09 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> + 17:05 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 11:29 [广告-深度转化] <|ad_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 16:14 [广告-深度转化] <|ad_begin|> + 17:00 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 10:44 [广告-深度转化] <|ad_begin|> + 13:12 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 06:49 [广告-深度转化] <|ad_begin|> + 18:38 [广告-深度转化] <|ad_begin|> + 19:15 [广告-深度转化] <|ad_begin|> + 19:41 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 01:52 [广告-深度转化] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 05:04 [广告-深度转化] <|ad_begin|> + 16:17 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:30 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 02:48 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 06:12 [广告-深度转化] <|ad_begin|> + 08:20 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 05:16 [广告-深度转化] <|ad_begin|> + 08:17 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> +【2026-01-14】 + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:32 [广告-深度转化] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> + 14:28 [广告-点击] <|ad_begin|> +【2026-01-15】 + 03:23 [广告-点击] <|ad_begin|> + 13:04 [广告-深度转化] <|ad_begin|> + 13:04 [广告-点击] <|ad_begin|> +【2026-01-16】 + 04:24 [广告-深度转化] <|ad_begin|> + 04:24 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 16:20 [广告-深度转化] <|ad_begin|> + 16:20 [广告-点击] <|ad_begin|> + 20:35 [广告-点击] <|ad_begin|> +【2026-01-17】 + 01:27 [广告-深度转化] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 03:32 [广告-深度转化] <|ad_begin|> + 03:32 [广告-点击] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 05:36 [广告-点击] <|ad_begin|> + 07:13 [广告-深度转化] <|ad_begin|> + 07:13 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> + 13:33 [广告-点击] <|ad_begin|> + 14:47 [广告-深度转化] <|ad_begin|> + 14:47 [广告-点击] <|ad_begin|> +【2026-01-18】 + 08:11 [广告-深度转化] <|ad_begin|> + 08:11 [广告-点击] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> +【2026-01-19】 + 11:47 [广告-点击] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> +【2026-01-20】 + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> +【2026-01-21】 + 15:51 [广告-深度转化] <|ad_begin|> + 15:51 [广告-点击] <|ad_begin|> +【2026-01-22】 + 00:28 [广告-深度转化] <|ad_begin|> + 00:28 [广告-点击] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + 04:37 [广告-深度转化] <|ad_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:41 [广告-点击] <|ad_begin|> + 14:46 [广告-点击] <|ad_begin|> + 17:44 [广告-点击] <|ad_begin|> + 20:38 [广告-点击] <|ad_begin|> +【2026-01-24】 + --:-- [商品-购买] <|prod_begin|> + 07:31 [广告-点击] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:12 [广告-点击] <|ad_begin|> + 12:37 [广告-点击] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:07 [广告-点击] <|ad_begin|> + 15:24 [广告-点击] <|ad_begin|> +【2026-01-25】 + 04:00 [广告-点击] <|ad_begin|> + 23:17 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:12 [广告-深度转化] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> + 18:12 [广告-深度转化] <|ad_begin|> + 18:12 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [商品-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|> + 09:33 [广告-深度转化] <|ad_begin|> + 09:33 [广告-点击] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:03 [广告-点击] <|ad_begin|> + 14:27 [视频-评论/长播] <|video_begin|> + 15:32 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 15:12 [视频-评论/长播] <|video_begin|> + 15:16 [视频-评论/长播] <|video_begin|> + 16:05 [视频-评论/长播] <|video_begin|> + 16:13 [视频-评论/长播] <|video_begin|> + 16:24 [广告-点击] <|ad_begin|> + 17:46 [视频-转发/长播] <|video_begin|> +【2026-01-30】 + --:-- [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|> + 04:18 [广告-点击] <|ad_begin|> +【2026-01-31】 + 05:00 [广告-深度转化] <|ad_begin|> + 05:00 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 14:54 [视频-转发/长播] <|video_begin|> + 17:10 [广告-点击] <|ad_begin|> + 17:11 [视频-评论/长播] <|video_begin|> + 17:40 [广告-点击] <|ad_begin|> +【2026-02-01】 + 00:48 [视频-评论/长播] <|video_begin|> + 02:09 [视频-评论/长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:30 [广告-点击] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 14:57 [视频-转发/点赞/长播] <|video_begin|> + 19:22 [视频-评论/长播] <|video_begin|> +【2026-02-02】 + 01:19 [视频-评论/长播] <|video_begin|> + 01:47 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:25 [视频-评论/长播] <|video_begin|> + 05:40 [广告-点击] <|ad_begin|> + 05:57 [广告-点击] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:47 [视频-评论/长播] <|video_begin|> + 08:10 [视频-评论/长播] <|video_begin|> + 12:04 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:37 [视频-评论/长播] <|video_begin|> + 06:47 [视频-浏览] <|video_begin|> + 06:54 [视频-评论/长播] <|video_begin|> + 07:01 [视频-评论/长播] <|video_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 13:23 [视频-评论/长播] <|video_begin|> + 14:47 [视频-评论/长播] <|video_begin|> + 15:20 [视频-评论/长播] <|video_begin|> + 16:58 [视频-评论/长播] <|video_begin|> + 17:21 [视频-评论/长播] <|video_begin|> + 17:39 [视频-浏览] <|video_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:43 [视频-评论/长播] <|video_begin|> + 20:53 [视频-评论/长播] <|video_begin|> + 21:10 [视频-评论/长播] <|video_begin|> + 21:18 [视频-评论/长播] <|video_begin|> + 21:25 [视频-评论/长播] <|video_begin|> + 21:35 [视频-评论/长播] <|video_begin|> + 21:39 [视频-评论/长播] <|video_begin|> + 21:44 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:59 [视频-评论/长播] <|video_begin|> + 22:12 [视频-评论/长播] <|video_begin|> + 23:19 [广告-点击] <|ad_begin|> + 23:52 [视频-浏览] <|video_begin|> +【2026-02-05】 + 00:37 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 01:08 [视频-收藏/长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:36 [视频-评论/长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:54 [视频-评论/长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 02:06 [视频-评论/长播] <|video_begin|> + 02:06 [视频-长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + 02:25 [视频-评论/长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:30 [视频-长播] <|video_begin|> + 02:40 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 02:51 [视频-评论/长播] <|video_begin|> + 03:11 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:37 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:09 [视频-评论/长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 05:45 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:32 [视频-评论/长播] <|video_begin|> + 06:32 [视频-长播] <|video_begin|> + 06:39 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:07 [视频-评论/长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:34 [视频-评论/长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 13:01 [广告-深度转化] <|ad_begin|> + 13:01 [广告-点击] <|ad_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 +核心逻辑要求（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 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +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：在同一主题类目下，交互 B 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请根据以下主题提取行为逻辑链： +主题：从被动接收信息到主动探索知识的兴趣演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收信息到主动探索知识的兴趣演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从被动接收信息到主动探索知识的兴趣演化", "events": [{"date": "2026-01-26", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"}, {"date": "2026-01-29", "action": "[视频-转发/长播] <|video_begin|>", "logic": "行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-02-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-02-04】 + --:-- [商品-购买] <|prod_begin|> +【2023-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2023-05-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-11-07】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-04-22】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-28】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-11-25】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-23】 + --:-- [直播-关注] <|living_begin|> +【2025-03-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-14】 + --:-- [直播-关注] <|living_begin|> +【2025-05-17】 + --:-- [直播-关注] <|living_begin|> +【2025-05-20】 + --:-- [直播-关注] <|living_begin|> +【2025-05-26】 + --:-- [直播-关注] <|living_begin|> +【2025-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-06】 + --:-- [直播-关注] <|living_begin|> +【2025-06-09】 + --:-- [直播-关注] <|living_begin|> +【2025-06-11】 + --:-- [直播-关注] <|living_begin|> +【2025-06-18】 + --:-- [直播-关注] <|living_begin|> +【2025-06-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-21】 + --:-- [直播-关注] <|living_begin|> +【2025-07-22】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-03】 + --:-- [直播-关注] <|living_begin|> +【2025-08-06】 + --:-- [直播-关注] <|living_begin|> +【2025-08-11】 + --:-- [直播-关注] <|living_begin|> +【2025-08-12】 + --:-- [直播-关注] <|living_begin|> +【2025-08-13】 + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-08-16】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> +【2025-08-20】 + --:-- [直播-关注] <|living_begin|> +【2025-09-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-05】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-09】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-10】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-13】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-26】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> +【2025-11-08】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-20】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-26】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-18】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-26】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + 10:34 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 14:44 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + --:-- [商品-点击] <|prod_begin|> + 16:13 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 12:51 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 11:52 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 15:13 [广告-深度转化] <|ad_begin|> + 15:33 [广告-深度转化] <|ad_begin|> + 15:34 [广告-深度转化] <|ad_begin|> + 16:13 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 04:11 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 00:40 [广告-深度转化] <|ad_begin|> + 03:54 [广告-深度转化] <|ad_begin|> + 16:27 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 08:33 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:55 [广告-深度转化] <|ad_begin|> + 12:36 [广告-深度转化] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-11】 + 06:45 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 03:46 [广告-深度转化] <|ad_begin|> + 06:13 [广告-深度转化] <|ad_begin|> + 09:10 [广告-深度转化] <|ad_begin|> + 11:25 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 00:10 [广告-深度转化] <|ad_begin|> + 15:40 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 13:30 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 14:02 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 07:20 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + 03:18 [广告-深度转化] <|ad_begin|> + 08:21 [广告-深度转化] <|ad_begin|> + 10:43 [广告-深度转化] <|ad_begin|> + 15:06 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:59 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-22】 + 07:37 [广告-深度转化] <|ad_begin|> + 09:54 [视频-点赞/长播] <|video_begin|> + 12:38 [广告-深度转化] <|ad_begin|> + 12:44 [广告-深度转化] <|ad_begin|> + 13:11 [广告-深度转化] <|ad_begin|> + 14:50 [广告-深度转化] <|ad_begin|> + 14:59 [广告-深度转化] <|ad_begin|> + 15:27 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:12 [广告-深度转化] <|ad_begin|> + 07:34 [广告-深度转化] <|ad_begin|> + 11:50 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 00:58 [广告-深度转化] <|ad_begin|> + 03:29 [广告-深度转化] <|ad_begin|> + 07:39 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 09:59 [广告-深度转化] <|ad_begin|> + 10:10 [广告-深度转化] <|ad_begin|> + 22:30 [广告-深度转化] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 13:56 [广告-深度转化] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> +【2026-01-28】 + 14:12 [广告-深度转化] <|ad_begin|> + 15:52 [广告-深度转化] <|ad_begin|> +【2026-01-29】 + 13:22 [广告-深度转化] <|ad_begin|> + 16:15 [视频-长播] <|video_begin|> + 16:48 [视频-长播] <|video_begin|> +【2026-01-30】 + 00:48 [视频-长播] <|video_begin|> + 00:48 [视频-长播] <|video_begin|> + 02:12 [视频-长播] <|video_begin|> + 03:29 [视频-长播] <|video_begin|> + 04:43 [视频-长播] <|video_begin|> + 07:53 [广告-深度转化] <|ad_begin|> + 09:09 [视频-长播] <|video_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:56 [视频-长播] <|video_begin|> + 10:26 [视频-长播] <|video_begin|> + 10:26 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 11:10 [视频-长播] <|video_begin|> + 12:01 [视频-长播] <|video_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 15:25 [视频-长播] <|video_begin|> + 15:45 [广告-深度转化] <|ad_begin|> + 20:53 [视频-长播] <|video_begin|> +【2026-01-31】 + 04:08 [视频-长播] <|video_begin|> + 04:08 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 07:52 [视频-长播] <|video_begin|> + 08:17 [广告-深度转化] <|ad_begin|> + 10:59 [视频-长播] <|video_begin|> + 14:35 [视频-长播] <|video_begin|> + 14:35 [视频-长播] <|video_begin|> + 15:36 [广告-点击] <|ad_begin|> + 23:41 [广告-点击] <|ad_begin|> + 23:43 [广告-点击] <|ad_begin|> + 23:47 [广告-点击] <|ad_begin|> + 23:49 [广告-点击] <|ad_begin|> + 23:50 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:54 [广告-点击] <|ad_begin|> + 23:57 [广告-点击] <|ad_begin|> +【2026-02-01】 + 01:29 [广告-点击] <|ad_begin|> + 02:03 [视频-长播] <|video_begin|> + 02:17 [广告-点击] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 08:22 [广告-点击] <|ad_begin|> + 08:32 [视频-长播] <|video_begin|> + 08:37 [视频-长播] <|video_begin|> + 10:00 [视频-长播] <|video_begin|> + 12:00 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 14:06 [广告-深度转化] <|ad_begin|> + 14:06 [广告-点击] <|ad_begin|> + 15:07 [视频-长播] <|video_begin|> + 15:27 [广告-点击] <|ad_begin|> + 15:31 [广告-点击] <|ad_begin|> + 15:31 [广告-点击] <|ad_begin|> + 15:33 [广告-点击] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> + 21:54 [视频-长播] <|video_begin|> + 21:54 [视频-长播] <|video_begin|> + 21:56 [广告-点击] <|ad_begin|> + 23:07 [视频-长播] <|video_begin|> +【2026-02-02】 + 00:16 [广告-点击] <|ad_begin|> + 00:21 [广告-点击] <|ad_begin|> + 00:22 [广告-点击] <|ad_begin|> + 00:24 [广告-点击] <|ad_begin|> + 04:40 [广告-点击] <|ad_begin|> + 07:47 [广告-点击] <|ad_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 10:07 [视频-长播] <|video_begin|> + 10:43 [广告-深度转化] <|ad_begin|> + 10:43 [广告-点击] <|ad_begin|> + 12:12 [广告-点击] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> + 14:55 [广告-点击] <|ad_begin|> + 20:53 [视频-长播] <|video_begin|> + 21:21 [视频-长播] <|video_begin|> + 23:54 [广告-点击] <|ad_begin|> + 23:55 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:00 [广告-点击] <|ad_begin|> + 00:01 [广告-点击] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> + 07:50 [广告-深度转化] <|ad_begin|> + 07:50 [广告-点击] <|ad_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 11:24 [广告-点击] <|ad_begin|> + 11:53 [视频-长播] <|video_begin|> + 11:53 [视频-长播] <|video_begin|> + 11:53 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 13:44 [广告-点击] <|ad_begin|> + 13:46 [广告-点击] <|ad_begin|> + 21:51 [广告-点击] <|ad_begin|> + 21:52 [广告-点击] <|ad_begin|> + 21:56 [广告-点击] <|ad_begin|> + 21:57 [广告-点击] <|ad_begin|> + 21:59 [广告-点击] <|ad_begin|> + 22:01 [广告-点击] <|ad_begin|> + 22:02 [广告-点击] <|ad_begin|> + 22:03 [广告-点击] <|ad_begin|> + 22:04 [广告-点击] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> +【2026-02-04】 + 01:12 [广告-点击] <|ad_begin|> + 04:04 [视频-长播] <|video_begin|> + 08:33 [视频-长播] <|video_begin|> + 10:30 [广告-点击] <|ad_begin|> + 10:59 [广告-点击] <|ad_begin|> + 11:37 [广告-深度转化] <|ad_begin|> + 11:37 [广告-点击] <|ad_begin|> + 23:43 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:56 [视频-长播] <|video_begin|> + 01:47 [视频-长播] <|video_begin|> + 02:59 [广告-深度转化] <|ad_begin|> + 02:59 [广告-点击] <|ad_begin|> + 03:50 [广告-点击] <|ad_begin|> + 03:51 [广告-点击] <|ad_begin|> + 03:52 [广告-点击] <|ad_begin|> + 03:56 [广告-点击] <|ad_begin|> + 03:57 [广告-点击] <|ad_begin|> + 06:05 [视频-长播] <|video_begin|> + 08:32 [广告-深度转化] <|ad_begin|> + 08:32 [广告-点击] <|ad_begin|> + 10:13 [广告-点击] <|ad_begin|> + 10:52 [广告-深度转化] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 12:29 [广告-深度转化] <|ad_begin|> + 12:29 [广告-点击] <|ad_begin|> + 14:35 [广告-点击] <|ad_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）： 视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +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 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请围绕以下主题提取行为逻辑链： +主题：从被动接收娱乐信息到主动探索专业知识的兴趣演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收娱乐信息到主动探索专业知识的兴趣演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这位用户的行为数据，以推断其兴趣是如何从被动接收娱乐信息，逐步演化到主动探索专业知识的。 + +从整体交互历史来看，我注意到这位用户的行为模式展现出一条清晰的演变轨迹。在早期，用户的消费行为主要集中在生活必需品和服饰上，例如购买妈妈装 <|prod_begin|>、女包 <|prod_begin|> 以及各类农产品和食品调料。这表明用户是一位注重家庭生活、有一定年纪的女性。 + +随着时间的推移，特别是在2025年，用户的兴趣开始明显地向直播内容倾斜。我发现，在2025年5月14日，用户关注了一个以剧情扮演和戏剧化直播为特点的主播 <|living_begin|>。这类直播内容娱乐性强，互动性高，主要是为了消遣和放松，属于一种比较被动的娱乐信息接收。这可以看作是用户兴趣的初始阶段，偏好轻松、有趣、有故事性的内容。 + +然而，一个显著的转折点出现在了2025年7月22日。在这一天，用户关注了一位标签为“中医养生达人”和“穴位教学专家”的主播 <|living_begin|>。这个行为非常关键，因为它标志着用户的兴趣从纯粹的娱乐消遣，开始转向具有实用价值和专业知识的领域。中医和养生这类内容需要用户投入更多的认知精力去理解和学习，不再是简单的被动观看，而是带有主动获取知识的目的。我推测，这可能是由于用户自身或家庭对健康问题的关注度提升，从而驱动了这一兴趣的迁移。 + +在建立了对中医养生的初步兴趣后，用户的探索行为变得更加深入和具体。大约一个月后，即2025年8月16日，用户再次关注了一位中医领域的讲师 <|living_begin|>。与前一位主播相比，这位主播的标签更为细化和专业，例如“心脑血管健康科普者”和“脾胃调理实操导师”。这表明用户的需求已经从泛泛的养生科普，升级到了针对特定健康问题的解决方案探索。这种从宏观到微观的兴趣深化，反映出用户正在主动构建自己的健康知识体系，而不仅仅是随机浏览。 + +综合来看，这位用户的兴趣演化路径清晰地展示了从被动娱乐到主动求知的转变。最初，用户被剧情化、娱乐性强的直播内容所吸引，这满足了其基础的消遣需求。随后，出于对健康问题的关注，用户的兴趣点转移到了实用性更强的中医养生领域，开始主动学习专业知识。最终，这种学习行为进一步深化，用户开始聚焦于更具体、更专业的健康调理方向，显示出一种系统性的知识探索倾向。 + +因此，我倾向于认为，用户的核心兴趣已经从早期的娱乐化直播内容，逐步演变为对专业健康知识的主动探索。这一演化过程始于对剧情扮演型主播的关注 <|living_begin|>，随后在关注中医养生达人 <|living_begin|> 时发生了关键性的转折，最终通过关注更具体的心脑血管及脾胃调理讲师 <|living_begin|> 得以深化和细化。未来，向其推荐具有深度、专业且可实操的健康养生类内容，有较大概率被其接纳和喜爱。 +{"logic_chain": {"name": "从被动接收娱乐信息到主动探索专业知识的兴趣演化", "events": [{"date": "2025-05-14", "action": "[直播-关注] <|living_begin|>", "logic": "初始阶段：兴趣主要集中在娱乐性、剧情化的直播内容，以消遣为主要目的。"}, {"date": "2025-07-22", "action": "[直播-关注] <|living_begin|>", "logic": "兴趣转折与深化：首次关注具有强知识属性的“中医养生”、“穴位教学”主播，标志着用户兴趣从纯娱乐转向实用性健康知识获取，实现了认知需求的跃迁。"}, {"date": "2025-08-16", "action": "[直播-关注] <|living_begin|>", "logic": "认知聚焦与细化：在已建立中医养生兴趣的基础上，进一步关注更具体、更深入的领域，如“心脑血管健康”、“脾胃调理实操”，表明用户正在构建更系统化的健康知识体系。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-08-08】 + --:-- [直播-关注] <|living_begin|> +【2025-08-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + 04:45 [广告-点击] <|ad_begin|> +【2025-12-11】 + 04:33 [广告-点击] <|ad_begin|> +【2025-12-12】 + 13:14 [广告-深度转化] <|ad_begin|> + 13:14 [广告-点击] <|ad_begin|> + 15:59 [广告-点击] <|ad_begin|> +【2025-12-13】 + 04:09 [广告-点击] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 07:11 [广告-点击] <|ad_begin|> + 07:15 [广告-点击] <|ad_begin|> + 09:37 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + 01:12 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + 11:48 [广告-点击] <|ad_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-19】 + 14:08 [广告-点击] <|ad_begin|> +【2025-12-20】 + 03:46 [广告-点击] <|ad_begin|> +【2025-12-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + 05:10 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-01】 + 02:01 [广告-点击] <|ad_begin|> + 08:25 [广告-深度转化] <|ad_begin|> + 08:25 [广告-点击] <|ad_begin|> +【2026-01-02】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-03】 + 09:17 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 10:17 [广告-点击] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-08】 + 11:53 [广告-点击] <|ad_begin|> + 12:00 [广告-点击] <|ad_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-09】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-10】 + 17:19 [广告-点击] <|ad_begin|> +【2026-01-11】 + 05:51 [广告-点击] <|ad_begin|> + 08:07 [广告-点击] <|ad_begin|> + 08:23 [广告-点击] <|ad_begin|> + 15:20 [广告-点击] <|ad_begin|> +【2026-01-13】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-17】 + 08:36 [广告-点击] <|ad_begin|> + 08:38 [广告-点击] <|ad_begin|> + 08:39 [广告-点击] <|ad_begin|> + 08:46 [广告-点击] <|ad_begin|> + 08:46 [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|> +【2026-01-19】 + --:-- [商品-加购] <|prod_begin|> +【2026-01-20】 + 06:04 [广告-深度转化] <|ad_begin|> + 06:04 [广告-点击] <|ad_begin|> + 07:06 [广告-深度转化] <|ad_begin|> + 07:06 [广告-点击] <|ad_begin|> + 23:57 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-22】 + 03:23 [视频-收藏/长播] <|video_begin|> + 03:35 [广告-点击] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 11:15 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + 04:50 [广告-点击] <|ad_begin|> + 15:01 [视频-收藏/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 10:39 [视频-收藏/长播] <|video_begin|> + 11:07 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 17:07 [视频-收藏/长播] <|video_begin|> +【2026-01-25】 + 04:19 [视频-关注/长播] <|video_begin|> + 05:13 [广告-点击] <|ad_begin|> + 05:52 [视频-收藏/长播] <|video_begin|> + 06:04 [视频-转发/长播] <|video_begin|> + 07:51 [广告-点击] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 15:28 [视频-转发/长播] <|video_begin|> + 16:48 [视频-评论/长播] <|video_begin|> + 16:55 [视频-评论/长播] <|video_begin|> +【2026-01-26】 + 01:33 [广告-深度转化] <|ad_begin|> + 01:33 [广告-点击] <|ad_begin|> + 03:13 [视频-评论/长播] <|video_begin|> + 09:11 [视频-评论/长播] <|video_begin|> + 09:31 [视频-收藏/长播] <|video_begin|> + 10:41 [视频-评论/长播] <|video_begin|> + 10:41 [视频-评论/长播] <|video_begin|> + 12:40 [视频-评论/长播] <|video_begin|> + 12:40 [视频-转发/长播] <|video_begin|> + 14:50 [视频-评论/长播] <|video_begin|> + 15:16 [视频-评论/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-27】 + --:-- [直播-首次打赏] <|living_begin|> + 03:33 [视频-收藏/长播] <|video_begin|> + 03:47 [视频-关注/长播] <|video_begin|> + 04:01 [视频-转发/长播] <|video_begin|> + 07:08 [视频-评论/长播] <|video_begin|> + 09:35 [视频-评论/长播] <|video_begin|> + 15:40 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + 02:41 [视频-评论/长播] <|video_begin|> + 04:09 [广告-点击] <|ad_begin|> + 04:51 [视频-评论/长播] <|video_begin|> + 05:33 [视频-评论/长播] <|video_begin|> + 16:31 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 02:05 [广告-点击] <|ad_begin|> + 04:09 [视频-评论/长播] <|video_begin|> + 04:09 [视频-收藏/长播] <|video_begin|> + 04:33 [视频-收藏/长播] <|video_begin|> + 05:52 [广告-点击] <|ad_begin|> + 08:17 [视频-转发/长播] <|video_begin|> + 10:41 [视频-评论/长播] <|video_begin|> + 12:05 [视频-评论/长播] <|video_begin|> + 13:16 [视频-评论/长播] <|video_begin|> + 15:06 [视频-评论/长播] <|video_begin|> +【2026-01-30】 + 08:39 [视频-转发/长播] <|video_begin|> + 15:22 [视频-评论/长播] <|video_begin|> + 16:32 [视频-评论/长播] <|video_begin|> +【2026-01-31】 + 03:16 [视频-评论/长播] <|video_begin|> + 03:16 [视频-收藏/长播] <|video_begin|> + 03:19 [视频-关注/长播] <|video_begin|> + 08:52 [广告-点击] <|ad_begin|> + 09:12 [视频-收藏/长播] <|video_begin|> + 15:09 [视频-评论/长播] <|video_begin|> + 16:20 [视频-收藏/长播] <|video_begin|> + 16:20 [视频-收藏/长播] <|video_begin|> + 16:24 [视频-评论/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 16:45 [视频-评论/长播] <|video_begin|> +【2026-02-01】 + 04:25 [视频-评论/长播] <|video_begin|> + 06:08 [视频-评论/长播] <|video_begin|> + 15:04 [视频-转发/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + 03:01 [视频-评论/长播] <|video_begin|> + 06:29 [视频-评论/长播] <|video_begin|> + 06:29 [视频-收藏/长播] <|video_begin|> + 08:36 [视频-评论/长播] <|video_begin|> + 10:17 [视频-评论/长播] <|video_begin|> + 14:08 [视频-评论/长播] <|video_begin|> + 15:46 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + 02:47 [广告-点击] <|ad_begin|> + 02:47 [广告-点击] <|ad_begin|> + 09:27 [广告-点击] <|ad_begin|> + 10:09 [视频-转发/评论/收藏/长播] <|video_begin|> + 14:33 [视频-收藏/长播] <|video_begin|> + 14:43 [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|> + 17:09 [视频-评论/长播] <|video_begin|> + 17:16 [视频-评论/长播] <|video_begin|> + 17:21 [视频-评论/长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 01:42 [视频-收藏/长播] <|video_begin|> + 01:47 [视频-评论/长播] <|video_begin|> + 02:31 [视频-评论/长播] <|video_begin|> + 05:22 [视频-评论/长播] <|video_begin|> + 05:54 [视频-评论/长播] <|video_begin|> + 05:59 [视频-评论/长播] <|video_begin|> + 07:26 [视频-评论/长播] <|video_begin|> + 08:12 [视频-转发/长播] <|video_begin|> + 12:59 [视频-评论/收藏/长播] <|video_begin|> + 15:55 [视频-评论/长播] <|video_begin|> + 15:58 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + 22:45 [视频-评论/长播] <|video_begin|> +【2026-02-05】 + 00:48 [广告-点击] <|ad_begin|> + 00:51 [广告-深度转化] <|ad_begin|> + 00:51 [广告-点击] <|ad_begin|> + 01:23 [视频-转发/点赞/长播] <|video_begin|> + 01:24 [广告-点击] <|ad_begin|> + 01:34 [视频-收藏/长播] <|video_begin|> + 02:26 [广告-点击] <|ad_begin|> + 03:22 [视频-收藏/长播] <|video_begin|> + 05:19 [视频-评论/长播] <|video_begin|> + 06:11 [视频-长播] <|video_begin|> + 06:11 [视频-长播] <|video_begin|> + 06:11 [视频-长播] <|video_begin|> + 06:18 [视频-长播] <|video_begin|> + 06:18 [视频-评论/长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 07:33 [视频-长播] <|video_begin|> + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:54 [广告-点击] <|ad_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:30 [视频-转发/长播] <|video_begin|> + 09:45 [视频-长播] <|video_begin|> + 09:45 [视频-长播] <|video_begin|> + 09:45 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 10:15 [广告-深度转化] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 10:26 [视频-长播] <|video_begin|> + 10:26 [视频-长播] <|video_begin|> + 10:30 [广告-点击] <|ad_begin|> + 10:53 [视频-长播] <|video_begin|> + 10:53 [视频-长播] <|video_begin|> + 11:01 [广告-点击] <|ad_begin|> + 11:01 [广告-点击] <|ad_begin|> + 11:34 [视频-长播] <|video_begin|> + 11:40 [视频-长播] <|video_begin|> + 11:40 [视频-长播] <|video_begin|> + 11:46 [视频-长播] <|video_begin|> + 11:46 [视频-长播] <|video_begin|> + 13:32 [视频-评论/长播] <|video_begin|> + 13:40 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:57 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:07 [视频-长播] <|video_begin|> + 15:07 [视频-长播] <|video_begin|> + 15:07 [视频-长播] <|video_begin|> + 15:32 [视频-长播] <|video_begin|> + 15:32 [视频-长播] <|video_begin|> + 15:32 [视频-评论/长播] <|video_begin|> + 15:37 [视频-长播] <|video_begin|> + 15:37 [视频-长播] <|video_begin|> + 15:40 [视频-长播] <|video_begin|> + 15:40 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> + 15:48 [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|> +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题提炼具有递进逻辑与内部关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心抽取逻辑（递进性准则）： +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 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请针对以下主题提取行为逻辑链： +主题：从泛化兴趣到精准购买的消费决策闭环 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化兴趣到精准购买的消费决策闭环", + "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任务。任务要求我基于用户的交互历史，挖掘出一条从产生兴趣到最终完成购买的消费决策链路。 + +从整体时间线来看，我注意到这位用户的消费行为相当广泛，涵盖了零食、香氛、个护、虚拟商品和电子产品等多个领域，显示出其兴趣点的多样性。在内容消费上，用户对游戏，特别是篮球类（如NBA2K）和射击类（如和平精英、CS:GO）表现出浓厚兴趣，同时也会关注一些生活、搞笑和情感类视频。直播方面，用户的偏好则比较分散，既有游戏主播，也有情感互动和才艺展示型主播。 + +为了找到一条清晰的“从泛化兴趣到精准购买”的行为链，我需要寻找一个从初步意向，经过信息搜集或兴趣强化，最终导向购买完成的完整过程。简单地购买零食或虚拟商品，虽然构成了消费，但往往缺少中间的决策强化环节，更像是即时性的需求满足。 + +在浏览2026年1月初的行为时，我发现了一个很有价值的信号。在1月2日，用户将一份泰国金枕榴莲肉 <|prod_begin|> 加入了购物车。这是一个非常明确的购买意向，但用户并没有立刻下单。这让我推测，他可能还在犹豫，或者在等待一个合适的时机，也可能是在寻找更多信息来坚定自己的购买决心。这个“加购”行为，可以看作是整个决策链路的起点。 + +接下来，我需要寻找能强化这个购买意向的行为。我仔细梳理了从1月2日到月底的所有交互记录。在这期间，用户消费了大量游戏和篮球相关的视频内容，也购买了一些游戏道具和电子产品，但这些都与榴莲这个品类无关。直到1月31日，一个关键行为出现了：用户关注并长时间观看了一个关于南丰榴莲加工厂的视频 <|video_begin|>。这个视频详细展示了榴莲从采摘、分类到包装的全过程，强调了果实的品质和加工的专业性，并且视频中还穿插了主播的推介和优惠信息。我认为，这个行为与月初的加购行为构成了强烈的因果关联。用户通过观看这个视频，很可能打消了之前对于产品品质、来源等方面的疑虑，视频内容极大地增强了他对榴莲产品的信任感。 + +最关键的一步，是决策的闭环。就在观看完榴莲加工厂视频的同一天，也就是1月31日，用户完成了购买行为，下单的正是月初加入购物车的那款金枕榴莲肉 <|prod_begin|>。这个时间上的紧密衔接，让我几乎可以肯定，观看视频是促使他最终下单的关键催化剂。从产生初步意向（加购），到通过内容消费进行信息确认和决策强化（观看视频），再到最终完成购买，这一系列行为构成了一个非常完整且逻辑清晰的消费决策闭环。 + +因此，我倾向于认为，用户的消费决策过程是：首先在1月2日产生了明确的购买意向，将榴莲 <|prod_begin|> 加入购物车；随后，在近一个月后的1月31日，通过观看榴莲加工厂的视频 <|video_begin|> 获取了更多产品信息，增强了购买信心；最终，在信息获取的当天完成了对该商品 <|prod_begin|> 的购买，形成了一个从兴趣到决策的完整闭环。 +{"logic_chain": {"name": "从泛化兴趣到精准购买的消费决策闭环", "events": [{"date": "2026-01-02", "action": "[商品-加购] <|prod_begin|>", "logic": "问题触发：产生明确的购买意向，将特定商品（金枕榴莲肉）加入购物车，但未立即下单，可能处于信息收集或决策犹豫阶段。"}, {"date": "2026-01-31", "action": "[视频-关注/长播] <|video_begin|>", "logic": "信息获取与决策强化：通过观看榴莲加工厂的视频，了解了产品的处理过程和品质特点，这种内容消费行为直接关联到之前的购买意向，起到了增强信任和促进决策的作用。"}, {"date": "2026-01-31", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在观看完相关视频的同一天，完成了对之前加购的同一商品的购买行为，形成了从意向到购买的完整闭环，视频观看是促成最终决策的关键环节。"}]}}<|im_end|> +<|im_start|>user +你具备理解商品token并生成高质量商品描述的能力。 +请解释本商品token<|prod_begin|>中的商品内容/no_think<|im_end|> +<|im_start|>assistant + + +这款竹叶青大方圆天然水晶石头镜，专为户外活动设计，镜框采用经典玳瑁纹路，兼具复古与时尚感，镜腿末端饰有皇冠标志，彰显精致细节。镜片为天然水晶材质，经特殊工艺打造，具备优异的防眩光性能，能有效提升户外环境下的视觉清晰度，尤其适合垂钓、登山、徒步等场景。作为一款防护型户外眼镜，它不仅提供物理遮挡，更强化了视觉舒适度与安全性。虽定位为户外应急装备，但其设计兼顾日常佩戴，适合追求品质与功能兼备的户外爱好者。商品售价1888元，由平台自营，支持无忧退换，是高端户外装备的优选。<|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, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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+我首先注意到，这位用户的行为在很长一段时间内展现出非常稳定且广泛的偏好。从2023年到2025年初，他的购物记录非常务实且多样化，涵盖了食品如酸菜鱼<|prod_begin|>和鸡爪<|prod_begin|>，家居用品如拖把<|prod_begin|>和垃圾袋<|prod_begin|>，以及一些工具类的商品，比如水平仪<|prod_begin|>、扳手<|prod_begin|>和电钻配件<|prod_begin|>。这些消费行为描绘出一个注重生活实用性的中年男性形象。同时，他购买的保健品如黄瓜籽粉<|prod_begin|>、耳耵聍清洗器<|prod_begin|>和各类抑菌膏、草本片剂，也暗示了他对身体健康的潜在关注。 + +进入2025年后，我发现他的兴趣开始明显向直播内容倾斜，尤其偏爱东北地区的主播。他关注的直播间<|living_begin|>、<|living_begin|>、<|living_begin|>等，大多带有浓厚的东北地域特色、接地气的唠嗑风格和才艺表演。这表明他在寻求一种娱乐化、有陪伴感的互动体验。 + +然而，一个显著的兴趣迁移发生在2026年1月下旬。从1月20日开始，我观察到他陷入了对短剧广告的高强度、沉浸式消费中。在短短十几天里，他深度转化或点击了大量的短剧广告，例如《斩仙台真人AI版》<|ad_begin|>、《阴阳神算》<|ad_begin|>、《假太监：掠夺气运从祸乱皇朝开始》<|ad_begin|>等等。这些广告内容多以古装、仙侠、权谋、情感纠葛为主题，剧情紧凑、冲突强烈。这种行为模式非常典型，属于被动地被信息流中的爽文式、快节奏娱乐内容所吸引，并持续进行消费。 + +就在这种高频次的娱乐内容消费浪潮中，我捕捉到了一个关键的转折点。在连续多日沉浸于短剧广告之后，到1月31日，他的行为突然出现了一次性质上的剧变。他主动关注了一个以“中医健康科普”和“痛风知识讲解”为核心内容的直播间<|living_begin|>。这个直播间的主播是“长发医师”，穿着白大褂进行“公益免费问诊”，内容严肃且专业。这次交互与之前被动刷短剧的娱乐行为形成了鲜明对比，从消磨时间的娱乐需求，一跃成为带有明确目的性的主动健康问询。我推测，之前购买保健品的行为只是浅层的健康意识，而这次主动关注专业医师的科普直播，标志着他的健康意识被真正唤醒，开始从被动接收信息转向主动寻求专业解答。 + +综合来看，我认为用户的行为演变路径是清晰的。他最初的消费行为体现了对生活实用性和身体健康的朴素关注。随后，他沉浸在信息流推荐的短剧广告中，表现为一种被动的娱乐内容消费。最终，这种状态被一次关键的行为转变所打破，他开始主动关注并寻求专业的健康知识，完成了从被动娱乐到主动健康问询的意识觉醒。 + +因此，我提炼出如下行为逻辑链： +最初，用户长时间沉浸在信息流推荐的短剧广告中，例如在2026年1月20日深度转化了《斩仙台真人AI版》<|ad_begin|>，这代表了他被动、高频的娱乐内容消费习惯。然而，在经历了连续多日的娱乐内容消费后，用户在2026年1月31日首次主动关注了一个以“中医健康科普”和“痛风知识讲解”为主题的专业医师直播间<|living_begin|>，这标志着他的注意力从被动娱乐显著转移至主动的健康问询，完成了一次关键的需求认知升级。 +{"logic_chain": {"name": "从被动信息流消费到主动健康问询的意识觉醒", "events": [{"date": "2026-01-20", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始状态：用户沉浸在信息流推荐的短剧广告中，行为模式为被动、高频的娱乐内容消费。"}, {"date": "2026-01-31", "action": "[直播-关注] <|living_begin|>", "logic": "行为转折与认知跃迁：在连续多日的娱乐内容轰炸后，用户行为首次出现断崖式转变，从被动接收娱乐信息转向主动关注严肃的“中医健康科普”、“痛风知识讲解”等专业内容，交互深度和意图明确性显著增强，标志着一次重要的注意力转移和需求觉醒。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从被动接收信息到主动探索知识的兴趣演化", "events": [{"date": "2026-01-26", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"}, {"date": "2026-01-29", "action": "[视频-转发/长播] <|video_begin|>", "logic": "行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|im_end|> + +好的，我来分析一下这位用户的行为数据，以推断其兴趣是如何从被动接收娱乐信息，逐步演化到主动探索专业知识的。 + +从整体交互历史来看，我注意到这位用户的行为模式展现出一条清晰的演变轨迹。在早期，用户的消费行为主要集中在生活必需品和服饰上，例如购买妈妈装 <|prod_begin|>、女包 <|prod_begin|> 以及各类农产品和食品调料。这表明用户是一位注重家庭生活、有一定年纪的女性。 + +随着时间的推移，特别是在2025年，用户的兴趣开始明显地向直播内容倾斜。我发现，在2025年5月14日，用户关注了一个以剧情扮演和戏剧化直播为特点的主播 <|living_begin|>。这类直播内容娱乐性强，互动性高，主要是为了消遣和放松，属于一种比较被动的娱乐信息接收。这可以看作是用户兴趣的初始阶段，偏好轻松、有趣、有故事性的内容。 + +然而，一个显著的转折点出现在了2025年7月22日。在这一天，用户关注了一位标签为“中医养生达人”和“穴位教学专家”的主播 <|living_begin|>。这个行为非常关键，因为它标志着用户的兴趣从纯粹的娱乐消遣，开始转向具有实用价值和专业知识的领域。中医和养生这类内容需要用户投入更多的认知精力去理解和学习，不再是简单的被动观看，而是带有主动获取知识的目的。我推测，这可能是由于用户自身或家庭对健康问题的关注度提升，从而驱动了这一兴趣的迁移。 + +在建立了对中医养生的初步兴趣后，用户的探索行为变得更加深入和具体。大约一个月后，即2025年8月16日，用户再次关注了一位中医领域的讲师 <|living_begin|>。与前一位主播相比，这位主播的标签更为细化和专业，例如“心脑血管健康科普者”和“脾胃调理实操导师”。这表明用户的需求已经从泛泛的养生科普，升级到了针对特定健康问题的解决方案探索。这种从宏观到微观的兴趣深化，反映出用户正在主动构建自己的健康知识体系，而不仅仅是随机浏览。 + +综合来看，这位用户的兴趣演化路径清晰地展示了从被动娱乐到主动求知的转变。最初，用户被剧情化、娱乐性强的直播内容所吸引，这满足了其基础的消遣需求。随后，出于对健康问题的关注，用户的兴趣点转移到了实用性更强的中医养生领域，开始主动学习专业知识。最终，这种学习行为进一步深化，用户开始聚焦于更具体、更专业的健康调理方向，显示出一种系统性的知识探索倾向。 + +因此，我倾向于认为，用户的核心兴趣已经从早期的娱乐化直播内容，逐步演变为对专业健康知识的主动探索。这一演化过程始于对剧情扮演型主播的关注 <|living_begin|>，随后在关注中医养生达人 <|living_begin|> 时发生了关键性的转折，最终通过关注更具体的心脑血管及脾胃调理讲师 <|living_begin|> 得以深化和细化。未来，向其推荐具有深度、专业且可实操的健康养生类内容，有较大概率被其接纳和喜爱。 +{"logic_chain": {"name": "从被动接收娱乐信息到主动探索专业知识的兴趣演化", "events": [{"date": "2025-05-14", "action": "[直播-关注] <|living_begin|>", "logic": "初始阶段：兴趣主要集中在娱乐性、剧情化的直播内容，以消遣为主要目的。"}, {"date": "2025-07-22", "action": "[直播-关注] <|living_begin|>", "logic": "兴趣转折与深化：首次关注具有强知识属性的“中医养生”、“穴位教学”主播，标志着用户兴趣从纯娱乐转向实用性健康知识获取，实现了认知需求的跃迁。"}, {"date": "2025-08-16", "action": "[直播-关注] <|living_begin|>", "logic": "认知聚焦与细化：在已建立中医养生兴趣的基础上，进一步关注更具体、更深入的领域，如“心脑血管健康”、“脾胃调理实操”，表明用户正在构建更系统化的健康知识体系。"}]}}<|im_end|> + +好的，我来分析一下这项推荐任务。任务要求我基于用户的交互历史，挖掘出一条从产生兴趣到最终完成购买的消费决策链路。 + +从整体时间线来看，我注意到这位用户的消费行为相当广泛，涵盖了零食、香氛、个护、虚拟商品和电子产品等多个领域，显示出其兴趣点的多样性。在内容消费上，用户对游戏，特别是篮球类（如NBA2K）和射击类（如和平精英、CS:GO）表现出浓厚兴趣，同时也会关注一些生活、搞笑和情感类视频。直播方面，用户的偏好则比较分散，既有游戏主播，也有情感互动和才艺展示型主播。 + +为了找到一条清晰的“从泛化兴趣到精准购买”的行为链，我需要寻找一个从初步意向，经过信息搜集或兴趣强化，最终导向购买完成的完整过程。简单地购买零食或虚拟商品，虽然构成了消费，但往往缺少中间的决策强化环节，更像是即时性的需求满足。 + +在浏览2026年1月初的行为时，我发现了一个很有价值的信号。在1月2日，用户将一份泰国金枕榴莲肉 <|prod_begin|> 加入了购物车。这是一个非常明确的购买意向，但用户并没有立刻下单。这让我推测，他可能还在犹豫，或者在等待一个合适的时机，也可能是在寻找更多信息来坚定自己的购买决心。这个“加购”行为，可以看作是整个决策链路的起点。 + +接下来，我需要寻找能强化这个购买意向的行为。我仔细梳理了从1月2日到月底的所有交互记录。在这期间，用户消费了大量游戏和篮球相关的视频内容，也购买了一些游戏道具和电子产品，但这些都与榴莲这个品类无关。直到1月31日，一个关键行为出现了：用户关注并长时间观看了一个关于南丰榴莲加工厂的视频 <|video_begin|>。这个视频详细展示了榴莲从采摘、分类到包装的全过程，强调了果实的品质和加工的专业性，并且视频中还穿插了主播的推介和优惠信息。我认为，这个行为与月初的加购行为构成了强烈的因果关联。用户通过观看这个视频，很可能打消了之前对于产品品质、来源等方面的疑虑，视频内容极大地增强了他对榴莲产品的信任感。 + +最关键的一步，是决策的闭环。就在观看完榴莲加工厂视频的同一天，也就是1月31日，用户完成了购买行为，下单的正是月初加入购物车的那款金枕榴莲肉 <|prod_begin|>。这个时间上的紧密衔接，让我几乎可以肯定，观看视频是促使他最终下单的关键催化剂。从产生初步意向（加购），到通过内容消费进行信息确认和决策强化（观看视频），再到最终完成购买，这一系列行为构成了一个非常完整且逻辑清晰的消费决策闭环。 + +因此，我倾向于认为，用户的消费决策过程是：首先在1月2日产生了明确的购买意向，将榴莲 <|prod_begin|> 加入购物车；随后，在近一个月后的1月31日，通过观看榴莲加工厂的视频 <|video_begin|> 获取了更多产品信息，增强了购买信心；最终，在信息获取的当天完成了对该商品 <|prod_begin|> 的购买，形成了一个从兴趣到决策的完整闭环。 +{"logic_chain": {"name": "从泛化兴趣到精准购买的消费决策闭环", "events": [{"date": "2026-01-02", "action": "[商品-加购] <|prod_begin|>", "logic": "问题触发：产生明确的购买意向，将特定商品（金枕榴莲肉）加入购物车，但未立即下单，可能处于信息收集或决策犹豫阶段。"}, {"date": "2026-01-31", "action": "[视频-关注/长播] <|video_begin|>", "logic": "信息获取与决策强化：通过观看榴莲加工厂的视频，了解了产品的处理过程和品质特点，这种内容消费行为直接关联到之前的购买意向，起到了增强信任和促进决策的作用。"}, {"date": "2026-01-31", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：在观看完相关视频的同一天，完成了对之前加购的同一商品的购买行为，形成了从意向到购买的完整闭环，视频观看是促成最终决策的关键环节。"}]}}<|im_end|> + + +这款竹叶青大方圆天然水晶石头镜，专为户外活动设计，镜框采用经典玳瑁纹路，兼具复古与时尚感，镜腿末端饰有皇冠标志，彰显精致细节。镜片为天然水晶材质，经特殊工艺打造，具备优异的防眩光性能，能有效提升户外环境下的视觉清晰度，尤其适合垂钓、登山、徒步等场景。作为一款防护型户外眼镜，它不仅提供物理遮挡，更强化了视觉舒适度与安全性。虽定位为户外应急装备，但其设计兼顾日常佩戴，适合追求品质与功能兼备的户外爱好者。商品售价1888元，由平台自营，支持无忧退换，是高端户外装备的优选。<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-17 01:41:16,198 >> 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] 2026-07-17 01:41:16,199 >> 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|2026-07-17 01:41:16] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-17 01:41:16] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-17 01:41:17,075 >> 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] 2026-07-17 01:41:17,075 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-17 01:41:17,124 >> 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] 2026-07-17 01:41:17,869 >> 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] 2026-07-17 01:41:18,218 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-17 01:41:18] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-17 01:41:18] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-17 01:41:18] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-17 01:41:18] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-17 01:41:18] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-17 01:41:18,247 >> 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] 2026-07-17 01:41:18,363 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-17 01:41:18,363 >> Num examples = 1,304 +[INFO|trainer.py:1471] 2026-07-17 01:41:18,363 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-17 01:41:18,363 >> Num update steps per epoch = 326 +[INFO|trainer.py:1473] 2026-07-17 01:41:18,363 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-17 01:41:18,363 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-17 01:41:18,363 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-17 01:41:18,363 >> Total optimization steps = 326 +[INFO|trainer.py:1479] 2026-07-17 01:41:18,364 >> Number of trainable parameters = 801,433,600 + 0%| | 0/326 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 326/326 [43:14<00:00, 7.71s/it] 100%|██████████| 326/326 [43:14<00:00, 7.96s/it] +[INFO|trainer.py:3815] 2026-07-17 02:24:33,082 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1 +[INFO|configuration_utils.py:533] 2026-07-17 02:24:33,083 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/config.json +[INFO|configuration_utils.py:816] 2026-07-17 02:24:33,083 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/generation_config.json +{'loss': '2.659', 'grad_norm': '8', 'learning_rate': '1.8e-05', 'epoch': '0.03067'} +{'loss': '2.114', 'grad_norm': '3.297', 'learning_rate': '1.996e-05', 'epoch': '0.06135'} +{'loss': '1.816', 'grad_norm': '2.984', 'learning_rate': '1.982e-05', 'epoch': '0.09202'} +{'loss': '1.794', 'grad_norm': '3.375', 'learning_rate': '1.959e-05', 'epoch': 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+{'loss': '1.565', 'grad_norm': '3.578', 'learning_rate': '1.089e-05', 'epoch': '0.4908'} +{'loss': '1.592', 'grad_norm': '2.672', 'learning_rate': '9.901e-06', 'epoch': '0.5215'} +{'loss': '1.572', 'grad_norm': '2.25', 'learning_rate': '8.909e-06', 'epoch': '0.5521'} +{'loss': '1.539', 'grad_norm': '2.625', 'learning_rate': '7.927e-06', 'epoch': '0.5828'} +{'loss': '1.527', 'grad_norm': '2.641', 'learning_rate': '6.967e-06', 'epoch': '0.6135'} +{'loss': '1.558', 'grad_norm': '3.016', 'learning_rate': '6.036e-06', 'epoch': '0.6442'} +{'loss': '1.567', 'grad_norm': '2.922', 'learning_rate': '5.144e-06', 'epoch': '0.6748'} +{'loss': '1.573', 'grad_norm': '2.438', 'learning_rate': '4.3e-06', 'epoch': '0.7055'} +{'loss': '1.514', 'grad_norm': '3.328', 'learning_rate': '3.513e-06', 'epoch': '0.7362'} +{'loss': '1.501', 'grad_norm': '3.047', 'learning_rate': '2.79e-06', 'epoch': '0.7669'} +{'loss': '1.585', 'grad_norm': '2.875', 'learning_rate': '2.138e-06', 'epoch': '0.7975'} +{'loss': '1.571', 'grad_norm': '2.641', 'learning_rate': '1.563e-06', 'epoch': '0.8282'} +{'loss': '1.531', 'grad_norm': '3.047', 'learning_rate': '1.072e-06', 'epoch': '0.8589'} +{'loss': '1.568', 'grad_norm': '2.781', 'learning_rate': '6.69e-07', 'epoch': '0.8896'} +{'loss': '1.53', 'grad_norm': '2.688', 'learning_rate': '3.581e-07', 'epoch': '0.9202'} +{'loss': '1.592', 'grad_norm': '2.75', 'learning_rate': '1.425e-07', 'epoch': '0.9509'} +{'loss': '1.512', 'grad_norm': '2.797', 'learning_rate': '2.421e-08', 'epoch': '0.9816'} +{'train_runtime': '2595', 'train_samples_per_second': '0.503', 'train_steps_per_second': '0.126', 'train_loss': '1.636', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-17 02:24:34,021 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-17 02:24:34,045 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 145593271GF + train_loss = 1.6356 + train_runtime = 0:43:14.71 + train_samples_per_second = 0.503 + train_steps_per_second = 0.126 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v52_public091_full_lr2e5_ep1/training_loss.png +[WARNING|2026-07-17 02:24:34] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-17 02:24:34] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-17 02:24:34,142 >> 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..57cd2d4 --- /dev/null +++ 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