From 816fcdc880422e54dc83241ae9fd71ba272f9bd0 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 19 Jul 2026 01:57:10 +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/v41_v29_baseline_general_replay_lr4e7_ep10 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 61 + generation_config.json | 12 + model.safetensors | 3 + ...62253.ubuntu-System-Product-Name.1384458.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2308 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 77 + trainer_state.json | 575 ++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 58530 bytes 17 files changed, 3323 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/Jul15_04-50-53_ubuntu-System-Product-Name/events.out.tfevents.1784062253.ubuntu-System-Product-Name.1384458.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.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..f59d5ad --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v29_v19_user_world_guard_lr8e7_ep018 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v41_v29_baseline_general_replay_lr4e7_ep10 + results: [] +--- + + + +# v41_v29_baseline_general_replay_lr4e7_ep10 + +This model is a fine-tuned version of [lldois/v29_v19_user_world_guard_lr8e7_ep018](https://huggingface.co/lldois/v29_v19_user_world_guard_lr8e7_ep018) on the exp_v41_baseline_general 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: 4e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607191 +- 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..4f80c43 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.68251671698516e+17, + "train_loss": 1.7103283592543677, + "train_runtime": 6160.8721, + "train_samples_per_second": 0.496, + "train_steps_per_second": 0.124 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..19185b5 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,61 @@ +{ + "created_at": "2026-07-15 00:59:15", + "name": "v41_v29_baseline_general_replay_lr4e7_ep10", + "base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v41_baseline_general", + "dataset_manifest": { + "name": "v41_baseline_general", + "path": "/home/ll/llm4rec/experiments/data/v41_baseline_general.jsonl", + "records": 80000, + "bytes": 482190982, + "sha256": "82e09565761de669ade898e8a1f743ab8e72da7e34b6effa1c780cdf12ecf007", + "requested_components": { + "general": 32000, + "r0": 8000, + "old_rec": 22000, + "old_user": 8000, + "old_item": 10000 + }, + "groups": { + "general": 32000, + "old_item": 10000, + "old_user": 8000, + "r0": 8000, + "old_rec": 22000 + }, + "variants": { + "official_general_cot": 15976, + "official_general_direct": 16024, + "original_competition": 19997, + "sid_to_caption_direct": 2051, + "item_no_think_direct_final": 1725, + "rec_no_think_direct_final": 11007, + "item_short_think": 1757, + "user_extra_no_think_logic": 514, + "caption_to_sid_direct": 3990, + "sid_to_caption_cot": 1959, + "user_strict_array": 1360, + "user_logic_json_strict": 1129, + "user_strict_logic": 1128, + "user_array_json_strict": 1383 + }, + "routes": { + "think": 35508, + "no_think": 44492 + }, + "target_leakage": 0, + "seed": 202607158 + }, + "learning_rate": "4.0e-7", + "epochs": 1.0, + "stages": null, + "note": "Control run dominated by original competition supervision and general replay, with no profile-derived R3.", + "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_v34_v43.py", + "script_sha256": "fbf01773c61579cf6159f64442fc0c8902714c84ed23b488bc9a6236a154f402", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v41_v29_baseline_general_replay_lr4e7_ep10.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_v34_v43.py --single v41_v29_baseline_general_replay_lr4e7_ep10 --gpu " +} \ 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..f598969 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6ae7d5ce183ee7336d42d903ce7a1a6b5214965560befa91d5536ad873d3b30 +size 1602902832 diff --git a/runs/Jul15_04-50-53_ubuntu-System-Product-Name/events.out.tfevents.1784062253.ubuntu-System-Product-Name.1384458.0 b/runs/Jul15_04-50-53_ubuntu-System-Product-Name/events.out.tfevents.1784062253.ubuntu-System-Product-Name.1384458.0 new file mode 100644 index 0000000..0888399 --- /dev/null +++ b/runs/Jul15_04-50-53_ubuntu-System-Product-Name/events.out.tfevents.1784062253.ubuntu-System-Product-Name.1384458.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1471c137c402e9809adedf41f52c6d867361cb90cf07e45615cc784842be511 +size 22310 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..35d445f --- /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": true, + "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..c89cc89 --- /dev/null +++ b/train.log @@ -0,0 +1,2308 @@ +[2026-07-15 04:50:17] stage 1/1: /home/ll/llm4rec/experiments/configs/v41_v29_baseline_general_replay_lr4e7_ep10.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|2026-07-15 04:50:20] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:769] 2026-07-15 04:50:20,525 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:50:20,529 >> Model config Qwen3Config { + "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 +} + +[INFO|configuration_utils.py:769] 2026-07-15 04:50:21,227 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:50:21,228 >> Model config Qwen3Config { + "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 +} + +[INFO|configuration_utils.py:769] 2026-07-15 04:50:21,228 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:50:21,228 >> Model config Qwen3Config { + "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 +} + +[INFO|2026-07-15 04:50:21] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v41_baseline_general.jsonl... +Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. + Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 17535 examples [00:00, 152225.77 examples/s] Generating train split: 36734 examples [00:00, 164238.32 examples/s] Generating train split: 57546 examples [00:00, 174234.93 examples/s] Generating train split: 78415 examples [00:00, 183532.67 examples/s] Generating train split: 80000 examples [00:00, 177815.07 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/80000 [00:00user +【用户交互历史】： +【2025-01-03】 + --:-- [直播-打赏] <|living_begin|> +【2025-01-05】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> +【2025-03-14】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-03】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-17】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-05】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-17】 + --:-- [直播-关注] <|living_begin|> +【2025-11-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> +【2025-11-23】 + --:-- [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [直播-关注] <|living_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-17】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-20】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + 06:40 [广告-深度转化] <|ad_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 18:55 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 03:43 [广告-深度转化] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 05:08 [广告-深度转化] <|ad_begin|> + 13:13 [广告-深度转化] <|ad_begin|> + 15:30 [广告-深度转化] <|ad_begin|> + 17:25 [广告-深度转化] <|ad_begin|> + 17:40 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 04:53 [广告-深度转化] <|ad_begin|> + 23:19 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-首次打赏] <|living_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 17:25 [广告-深度转化] <|ad_begin|> + 17:40 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 03:27 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:22 [广告-深度转化] <|ad_begin|> + 16:32 [广告-点击] <|ad_begin|> + 16:42 [广告-深度转化] <|ad_begin|> + 16:42 [广告-点击] <|ad_begin|> + 19:38 [广告-深度转化] <|ad_begin|> + 19:38 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 09:06 [广告-深度转化] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + 03:02 [广告-深度转化] <|ad_begin|> + 03:02 [广告-点击] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 05:07 [广告-点击] <|ad_begin|> + 05:41 [广告-深度转化] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + 08:07 [广告-深度转化] <|ad_begin|> + 08:07 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 08:31 [广告-深度转化] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 09:01 [广告-深度转化] <|ad_begin|> + 09:01 [广告-点击] <|ad_begin|> + 16:06 [广告-深度转化] <|ad_begin|> + 16:06 [广告-点击] <|ad_begin|> + 17:42 [广告-深度转化] <|ad_begin|> + 17:42 [广告-点击] <|ad_begin|> + 17:52 [广告-点击] <|ad_begin|> + 18:04 [广告-深度转化] <|ad_begin|> + 18:04 [广告-点击] <|ad_begin|> +【2026-01-11】 + 04:38 [广告-深度转化] <|ad_begin|> + 04:38 [广告-点击] <|ad_begin|> +【2026-01-12】 + --:-- [直播-打赏] <|living_begin|> + 06:35 [广告-深度转化] <|ad_begin|> + 06:35 [广告-点击] <|ad_begin|> + 15:36 [广告-点击] <|ad_begin|> +【2026-01-13】 + 03:09 [广告-深度转化] <|ad_begin|> + 03:09 [广告-点击] <|ad_begin|> + 04:48 [广告-深度转化] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 09:54 [广告-深度转化] <|ad_begin|> + 09:54 [广告-点击] <|ad_begin|> + 11:11 [广告-深度转化] <|ad_begin|> + 11:11 [广告-点击] <|ad_begin|> + 13:18 [广告-深度转化] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-首次打赏] <|living_begin|> + 04:22 [广告-深度转化] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> + 17:10 [广告-深度转化] <|ad_begin|> + 17:10 [广告-点击] <|ad_begin|> + 20:10 [广告-深度转化] <|ad_begin|> + 20:10 [广告-点击] <|ad_begin|> +【2026-01-16】 + 03:13 [广告-深度转化] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + 03:11 [广告-点击] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 18:31 [广告-深度转化] <|ad_begin|> + 18:31 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:13 [广告-点击] <|ad_begin|> + 18:02 [广告-深度转化] <|ad_begin|> + 18:02 [广告-点击] <|ad_begin|> +【2026-01-21】 + 03:26 [广告-深度转化] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> + 03:41 [广告-深度转化] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> + 09:29 [广告-深度转化] <|ad_begin|> + 09:29 [广告-点击] <|ad_begin|> + 09:43 [广告-深度转化] <|ad_begin|> + 09:43 [广告-点击] <|ad_begin|> + 17:29 [广告-深度转化] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> +【2026-01-22】 + 11:11 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:08 [视频-点赞/长播] <|video_begin|> + 18:03 [广告-点击] <|ad_begin|> + 18:10 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + 04:00 [视频-点赞/长播] <|video_begin|> + 07:08 [广告-深度转化] <|ad_begin|> + 07:08 [广告-点击] <|ad_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:35 [广告-深度转化] <|ad_begin|> + 11:35 [广告-点击] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 15:48 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:16 [视频-点赞/长播] <|video_begin|> + 15:16 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:42 [视频-点赞/长播] <|video_begin|> + 09:07 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:37 [视频-浏览] <|video_begin|> + 17:17 [广告-深度转化] <|ad_begin|> + 17:17 [广告-点击] <|ad_begin|> + 21:19 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:11 [广告-点击] <|ad_begin|> + 10:08 [广告-深度转化] <|ad_begin|> + 10:08 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:55 [视频-转发/长播] <|video_begin|> + 18:08 [视频-点赞/长播] <|video_begin|> + 18:10 [视频-点赞/长播] <|video_begin|> + 18:14 [视频-评论/点赞/长播] <|video_begin|> + 19:26 [广告-深度转化] <|ad_begin|> + 19:26 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 03:35 [视频-浏览] <|video_begin|> + 09:29 [广告-点击] <|ad_begin|> + 15:10 [视频-点赞/长播] <|video_begin|> + 15:10 [视频-点赞/长播] <|video_begin|> + 16:20 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 03:29 [视频-浏览] <|video_begin|> + 03:54 [广告-深度转化] <|ad_begin|> + 03:54 [广告-点击] <|ad_begin|> + 04:05 [广告-深度转化] <|ad_begin|> + 04:05 [广告-点击] <|ad_begin|> + 07:21 [广告-深度转化] <|ad_begin|> + 07:21 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:19 [视频-点赞/长播] <|video_begin|> +【2026-01-29】 + 03:06 [视频-点赞/长播] <|video_begin|> + 03:24 [视频-点赞/长播] <|video_begin|> + 10:36 [视频-关注/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:23 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 17:53 [视频-点赞/长播] <|video_begin|> + 20:59 [视频-点赞/长播] <|video_begin|> + 21:13 [视频-点赞/长播] <|video_begin|> + 22:48 [视频-点赞/长播] <|video_begin|> + 23:03 [视频-点赞/长播] <|video_begin|> + 23:17 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 04:00 [视频-点赞/长播] <|video_begin|> + 04:11 [视频-点赞/长播] <|video_begin|> + 04:26 [广告-点击] <|ad_begin|> + 10:51 [视频-点赞/长播] <|video_begin|> + 10:55 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 17:48 [视频-点赞/长播] <|video_begin|> + 17:58 [广告-深度转化] <|ad_begin|> + 17:58 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [直播-首次打赏] <|living_begin|> + 03:37 [广告-点击] <|ad_begin|> + 03:52 [广告-深度转化] <|ad_begin|> + 03:52 [广告-点击] <|ad_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 18:20 [广告-深度转化] <|ad_begin|> + 18:20 [广告-点击] <|ad_begin|> + 18:39 [视频-点赞/长播] <|video_begin|> + 18:50 [广告-深度转化] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> + 19:31 [视频-点赞/长播] <|video_begin|> + 19:40 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 06:11 [广告-深度转化] <|ad_begin|> + 06:11 [广告-点击] <|ad_begin|> + 15:14 [广告-深度转化] <|ad_begin|> + 15:14 [广告-点击] <|ad_begin|> + 15:44 [广告-点击] <|ad_begin|> + 16:27 [视频-点赞/长播] <|video_begin|> + 16:27 [视频-点赞/长播] <|video_begin|> + 18:07 [视频-点赞/长播] <|video_begin|> + 18:22 [广告-深度转化] <|ad_begin|> + 18:22 [广告-点击] <|ad_begin|> + 21:04 [广告-点击] <|ad_begin|> +【2026-02-02】 + 18:11 [视频-点赞/长播] <|video_begin|> + 19:11 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 02:35 [广告-深度转化] <|ad_begin|> + 02:35 [广告-点击] <|ad_begin|> + 03:17 [视频-点赞/长播] <|video_begin|> + 03:20 [视频-点赞/长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 09:27 [视频-长播] <|video_begin|> + 12:34 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:39 [视频-长播] <|video_begin|> + 13:39 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 15:12 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-首次打赏] <|living_begin|> + 01:56 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 20:36 [视频-长播] <|video_begin|> + 20:38 [视频-长播] <|video_begin|> + 20:38 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:28 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:38 [视频-长播] <|video_begin|> + 22:45 [视频-长播] <|video_begin|> + 23:03 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:17 [视频-长播] <|video_begin|> + 23:22 [视频-长播] <|video_begin|> + 23:22 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> +【2026-02-05】 + 04:48 [视频-长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:25 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> +角色任务：你是一个极端严苛的用户行为数据挖掘专家以及数据格式化专家，请根据以上用户交互历史，针对给定的主题，提取出具有行为递进逻辑和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请面向以下主题提取行为逻辑链： +主题：从泛化个人形象提升到具体化社交工具探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化个人形象提升到具体化社交工具探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从泛化个人形象提升到具体化社交工具探索","events":[{"date":"2025-06-14","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：开始关注带有“香氛”属性的个人护理产品，初步探索通过气味提升个人形象。"},{"date":"2025-09-01","action":"[商品-购买] <|prod_begin|>","logic":"需求强化：复购同类产品，但商品描述从“深层清洁”升级为“洗出伪体香”、“激发魅力”，表明用户对气味社交功能的认知加深，并追求更强的效果。"},{"date":"2025-09-04","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与行为升级：意识到沐浴露的留香效果有限，首次转向更专业、持久的香氛产品——香水。从被动、短暂的“洗护留香”升级为主动、专业的“穿戴香气”，完成了从基础个护到专业社交工具的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2026-01-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-06】 + 12:13 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-08】 + 04:26 [广告-深度转化] <|ad_begin|> + 04:26 [广告-深度转化] <|ad_begin|> + 09:12 [广告-深度转化] <|ad_begin|> + 12:21 [广告-深度转化] <|ad_begin|> + 12:21 [广告-深度转化] <|ad_begin|> + 12:36 [广告-深度转化] <|ad_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 13:40 [广告-深度转化] <|ad_begin|> + 15:05 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 02:07 [广告-深度转化] <|ad_begin|> + 02:36 [广告-深度转化] <|ad_begin|> + 14:34 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + 03:52 [广告-深度转化] <|ad_begin|> + 07:07 [广告-深度转化] <|ad_begin|> + 10:52 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [直播-首次打赏] <|living_begin|> + 08:29 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-首次打赏] <|living_begin|> + 05:24 [广告-深度转化] <|ad_begin|> + 06:48 [广告-深度转化] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 05:29 [广告-深度转化] <|ad_begin|> + 12:36 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 09:12 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 03:02 [广告-深度转化] <|ad_begin|> + 04:02 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 14:12 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + --:-- [商品-购买] <|prod_begin|> + 11:17 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 01:55 [广告-深度转化] <|ad_begin|> + 07:54 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + 01:39 [广告-深度转化] <|ad_begin|> + 02:11 [广告-深度转化] <|ad_begin|> + 02:26 [广告-深度转化] <|ad_begin|> + 06:41 [广告-深度转化] <|ad_begin|> + 12:51 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 00:22 [广告-深度转化] <|ad_begin|> + 01:07 [广告-深度转化] <|ad_begin|> + 08:43 [广告-深度转化] <|ad_begin|> + 09:00 [广告-深度转化] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> + 11:29 [广告-深度转化] <|ad_begin|> + 11:45 [广告-深度转化] <|ad_begin|> + 12:08 [广告-深度转化] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 14:20 [广告-深度转化] <|ad_begin|> + 14:44 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 00:32 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:47 [广告-深度转化] <|ad_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 11:31 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + 11:27 [广告-深度转化] <|ad_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:42 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:29 [视频-点赞/长播] <|video_begin|> + 05:18 [视频-点赞/长播] <|video_begin|> + 05:43 [视频-点赞/长播] <|video_begin|> + 22:30 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-24】 + 07:38 [视频-点赞/长播] <|video_begin|> + 11:15 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + 09:39 [视频-收藏/长播] <|video_begin|> +【2026-01-26】 + 08:30 [广告-点击] <|ad_begin|> + 12:40 [广告-点击] <|ad_begin|> + 13:53 [广告-点击] <|ad_begin|> + 23:37 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:43 [广告-点击] <|ad_begin|> + 10:44 [广告-点击] <|ad_begin|> + 11:31 [广告-深度转化] <|ad_begin|> + 11:31 [广告-点击] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> + 12:44 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:59 [视频-点赞/长播] <|video_begin|> + 08:35 [广告-点击] <|ad_begin|> + 08:36 [视频-点赞/长播] <|video_begin|> + 08:36 [广告-点击] <|ad_begin|> + 08:56 [视频-点赞/长播] <|video_begin|> + 12:25 [视频-关注/点赞/长播] <|video_begin|> + 12:55 [视频-关注/点赞/长播] <|video_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:49 [视频-点赞/长播] <|video_begin|> + 14:04 [广告-点击] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:05 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:14 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:09 [视频-关注/长播] <|video_begin|> + 04:09 [视频-点赞/长播] <|video_begin|> + 06:04 [广告-点击] <|ad_begin|> + 07:27 [广告-点击] <|ad_begin|> + 07:30 [视频-点赞/长播] <|video_begin|> + 07:34 [广告-点击] <|ad_begin|> + 09:31 [广告-点击] <|ad_begin|> + 09:37 [广告-点击] <|ad_begin|> + 09:43 [广告-点击] <|ad_begin|> + 10:58 [广告-深度转化] <|ad_begin|> + 10:58 [广告-点击] <|ad_begin|> + 11:13 [广告-点击] <|ad_begin|> + 11:37 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 11:58 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:42 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:59 [视频-收藏/长播] <|video_begin|> + 03:29 [广告-点击] <|ad_begin|> + 03:44 [广告-点击] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + 14:45 [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|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:31 [视频-关注/点赞/长播] <|video_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 06:41 [广告-深度转化] <|ad_begin|> + 06:41 [广告-点击] <|ad_begin|> + 07:35 [广告-点击] <|ad_begin|> + 07:45 [视频-关注/长播] <|video_begin|> + 08:02 [视频-关注/长播] <|video_begin|> + 08:14 [广告-点击] <|ad_begin|> + 09:27 [视频-关注/长播] <|video_begin|> + 09:30 [视频-点赞/长播] <|video_begin|> + 09:44 [视频-关注/长播] <|video_begin|> + 09:52 [视频-收藏/长播] <|video_begin|> + 10:08 [视频-关注/长播] <|video_begin|> + 10:09 [视频-关注/长播] <|video_begin|> + 10:36 [视频-关注/长播] <|video_begin|> + 11:34 [广告-点击] <|ad_begin|> + 11:58 [广告-点击] <|ad_begin|> + 12:19 [广告-点击] <|ad_begin|> + 12:46 [视频-关注/长播] <|video_begin|> + 12:46 [视频-关注/长播] <|video_begin|> + 12:46 [视频-点赞/长播] <|video_begin|> + 14:11 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 00:31 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:24 [视频-点赞/长播] <|video_begin|> + 11:11 [视频-关注/长播] <|video_begin|> + 11:13 [广告-点击] <|ad_begin|> + 11:31 [广告-点击] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + 13:10 [视频-关注/点赞/长播] <|video_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 13:22 [广告-点击] <|ad_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:43 [视频-关注/长播] <|video_begin|> + 04:43 [视频-点赞/长播] <|video_begin|> + 05:16 [视频-点赞/长播] <|video_begin|> + 05:46 [广告-深度转化] <|ad_begin|> + 05:46 [广告-点击] <|ad_begin|> + 09:30 [广告-点击] <|ad_begin|> + 10:04 [广告-深度转化] <|ad_begin|> + 10:04 [广告-点击] <|ad_begin|> + 10:28 [视频-点赞/长播] <|video_begin|> + 14:00 [视频-关注/长播] <|video_begin|> + 14:00 [视频-点赞/长播] <|video_begin|> + 14:42 [视频-关注/长播] <|video_begin|> +【2026-02-03】 + 00:51 [视频-关注/长播] <|video_begin|> + 01:11 [视频-关注/长播] <|video_begin|> + 04:48 [广告-点击] <|ad_begin|> + 05:36 [视频-点赞/长播] <|video_begin|> + 06:02 [广告-点击] <|ad_begin|> + 06:11 [视频-点赞/长播] <|video_begin|> + 09:14 [视频-转发/长播] <|video_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 09:45 [广告-点击] <|ad_begin|> + 09:46 [视频-关注/长播] <|video_begin|> + 09:48 [视频-关注/长播] <|video_begin|> + 09:48 [视频-关注/长播] <|video_begin|> + 09:56 [视频-关注/长播] <|video_begin|> + 10:00 [视频-关注/长播] <|video_begin|> + 10:47 [视频-关注/长播] <|video_begin|> + 12:17 [广告-深度转化] <|ad_begin|> + 12:17 [广告-点击] <|ad_begin|> + 12:19 [广告-点击] <|ad_begin|> + 14:17 [广告-深度转化] <|ad_begin|> + 14:17 [广告-点击] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:59 [视频-点赞/长播] <|video_begin|> + 05:14 [广告-点击] <|ad_begin|> + 06:00 [视频-关注/长播] <|video_begin|> + 07:04 [视频-关注/长播] <|video_begin|> + 07:15 [视频-关注/长播] <|video_begin|> + 10:46 [广告-点击] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> + 11:25 [广告-点击] <|ad_begin|> + 11:26 [广告-点击] <|ad_begin|> + 11:35 [视频-关注/长播] <|video_begin|> + 11:35 [视频-点赞/长播] <|video_begin|> + 13:09 [视频-关注/长播] <|video_begin|> + 13:40 [广告-点击] <|ad_begin|> + 13:44 [广告-点击] <|ad_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> + 23:59 [视频-关注/长播] <|video_begin|> +【2026-02-05】 + 00:07 [视频-关注/长播] <|video_begin|> + 00:10 [广告-点击] <|ad_begin|> + 00:10 [视频-关注/长播] <|video_begin|> + 00:11 [视频-关注/长播] <|video_begin|> + 00:11 [视频-点赞/长播] <|video_begin|> + 00:27 [视频-关注/长播] <|video_begin|> + 00:30 [视频-关注/长播] <|video_begin|> + 00:35 [视频-关注/长播] <|video_begin|> + 00:35 [视频-关注/长播] <|video_begin|> + 00:42 [视频-关注/长播] <|video_begin|> + 00:48 [视频-关注/长播] <|video_begin|> + 00:50 [视频-关注/长播] <|video_begin|> + 00:50 [视频-关注/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:49 [视频-关注/长播] <|video_begin|> + 01:55 [视频-关注/长播] <|video_begin|> + 02:03 [视频-点赞/长播] <|video_begin|> + 02:10 [广告-点击] <|ad_begin|> + 02:23 [广告-深度转化] <|ad_begin|> + 02:23 [广告-点击] <|ad_begin|> + 02:36 [广告-点击] <|ad_begin|> + 03:58 [广告-点击] <|ad_begin|> + 06:10 [视频-关注/长播] <|video_begin|> + 06:20 [视频-关注/长播] <|video_begin|> + 06:25 [视频-关注/长播] <|video_begin|> + 06:25 [视频-关注/长播] <|video_begin|> + 06:41 [视频-长播] <|video_begin|> + 06:41 [视频-长播] <|video_begin|> + 06:41 [视频-长播] <|video_begin|> + 06:41 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:43 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 06:49 [广告-点击] <|ad_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 11:43 [视频-长播] <|video_begin|> + 11:52 [广告-点击] <|ad_begin|> + 13:40 [视频-长播] <|video_begin|> + 13:40 [视频-长播] <|video_begin|> + 13:40 [视频-长播] <|video_begin|> + 13:41 [视频-关注/长播] <|video_begin|> + 13:41 [视频-长播] <|video_begin|> + 13:41 [视频-长播] <|video_begin|> + 13:41 [视频-长播] <|video_begin|> + 13:41 [视频-长播] <|video_begin|> + 13:41 [广告-点击] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> + 13:54 [视频-长播] <|video_begin|> + 13:54 [视频-长播] <|video_begin|> + 13:54 [视频-长播] <|video_begin|> + 13:54 [视频-长播] <|video_begin|> + 13:54 [视频-长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 14:00 [广告-点击] <|ad_begin|> + 14:02 [广告-点击] <|ad_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:03 [视频-长播] <|video_begin|> + 14:08 [视频-长播] <|video_begin|> + 14:08 [视频-长播] <|video_begin|> + 14:54 [视频-长播] <|video_begin|> + 14:54 [视频-长播] <|video_begin|> + 15:03 [视频-关注/长播] <|video_begin|> + 15:04 [视频-长播] <|video_begin|> + 15:04 [视频-长播] <|video_begin|> + 15:05 [视频-长播] <|video_begin|> + 15:05 [视频-长播] <|video_begin|> + 15:05 [视频-长播] <|video_begin|> + 15:05 [视频-长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:17 [视频-长播] <|video_begin|> + 15:17 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心提取逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 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 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从泛化工具认知到精准化电路检测工具的选购闭环 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化工具认知到精准化电路检测工具的选购闭环", + "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，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接到的任务是，基于用户的交互历史，挖掘出一条从泛化工具认知到精准化电路检测工具选购的行为逻辑链。这意味着我需要从用户大量的行为数据中，找到一个清晰的、由浅入深的兴趣演变过程，最终导向一个具体的购买决策。 + +首先，我快速浏览了用户的整体行为时间线，发现其兴趣点非常广泛，涵盖了美女直播、情感内容、小说广告、休闲游戏以及各种生活日用品等。在如此庞杂的兴趣中，要找到一条关于“电路检测工具”的线索，我必须聚焦于那些与工具、维修、DIY相关的交互点。 + +在时间线的后期，我注意到了一些关键信号。在2月3日，用户转发并长时间观看了一个关于厨房装修的视频 <|video_begin|>。这个视频内容非常具体，展示了专业技术人员使用电钻、螺丝刀等工具进行管道铺设。我认为，这很可能是用户对这类工具产生初步认知的起点。虽然他可能之前也见过这些工具，但这次“转发”和“长播”的强交互行为，表明他对这个场景下的专业操作产生了较高的兴趣，可能由此意识到了在家居维修中拥有合适工具的重要性。 + +有了这个起点，我继续向后追溯。在接下来的两天里，用户的兴趣似乎又回到了他更常关注的美女、社交和生活内容上。然而，到了2月5日，一个关键的视频 <|video_begin|> 再次出现。这个视频专门介绍了“家用强扭力测电笔”，详细讲解了它如何检测电路、解决故障，甚至还提到了它兼具螺丝刀功能。我推断，这是用户兴趣的一次重要收敛和深化。从之前对“电钻”这类泛化电动工具的认知，他现在开始聚焦于“测电笔”这一更具体、更具诊断功能的专业工具。这次长播行为表明，他正在主动学习和了解这类工具的具体用途和价值。 + +这个发现让我非常兴奋，因为一条清晰的“认知-深化”路径已经浮现。接下来，我需要寻找将这种认知转化为实际购买行动的证据。我将目光锁定在2月6日，也就是观看测电笔介绍视频的第二天。果然，在这一天的商品交互记录中，我找到了决定性的闭环行为。用户不仅点击了多款测电笔商品，如 <|prod_begin|> 和 <|prod_begin|>，最终还明确地购买了其中一款具备“高亮感应”、“智能”、“测断点”等高级功能的新款测电笔 <|prod_begin|>。 + +这一系列的购买行为，完美地印证了我的推测。用户的需求已经从“了解”升级为“拥有”，并且他的选择非常精准，不再是泛泛地浏览，而是直接购买了功能更强、更智能化的产品。这表明前两天的视频内容已经成功地教育了用户，让他明确了自己的需求，从而做出了高效的购买决策。 + +综合来看，这条行为链条非常完整且逻辑递进关系清晰。它始于一个偶然的装修视频，触发了对专业工具的泛化认知；随后通过一个专门介绍视频，将兴趣深化并聚焦到测电笔这一细分品类上；最终在短时间内迅速转化为对一款功能明确的智能测电笔的购买行为，形成了一个从认知到决策的完整闭环。因此，我倾向于认为，这条路径精准地反映了用户在该主题下的兴趣演进和最终的消费转化。 +{"logic_chain": {"name": "从泛化工具认知到精准化电路检测工具的选购闭环", "events": [{"date": "2026-02-03", "action": "[视频-转发/长播] <|video_begin|>", "logic": "问题触发：通过观看装修视频，接触到“电钻”等电工工具的使用场景，形成初步认知。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知深化：兴趣从通用电动工具收敛至“测电笔”这一特定诊断工具，并开始了解其核心功能（检测电路、解决故障）。"}, {"date": "2026-02-06", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：从了解基础功能转向购买具备“感应”、“智能”、“测断点”等高级功能的具体商品，体现了基于知识获取后的精准决策和消费转化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-06】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-01】 + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-20】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-23】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-14】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 19:08 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:53 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 03:37 [视频-点赞/长播] <|video_begin|> + 12:02 [广告-深度转化] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 12:45 [广告-深度转化] <|ad_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 15:20 [视频-长播] <|video_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 18:51 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 21:27 [广告-深度转化] <|ad_begin|> + 23:42 [视频-长播] <|video_begin|> +【2026-01-23】 + 11:38 [视频-长播] <|video_begin|> + 12:14 [广告-深度转化] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 13:05 [广告-深度转化] <|ad_begin|> + 13:30 [广告-深度转化] <|ad_begin|> + 14:33 [广告-深度转化] <|ad_begin|> + 18:01 [广告-深度转化] <|ad_begin|> + 21:04 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 08:51 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 12:00 [广告-深度转化] <|ad_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:23 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:37 [视频-长播] <|video_begin|> + 12:41 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 16:33 [广告-深度转化] <|ad_begin|> + 17:14 [广告-深度转化] <|ad_begin|> + 18:02 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:20 [广告-深度转化] <|ad_begin|> +【2026-01-25】 + 02:13 [广告-深度转化] <|ad_begin|> + 02:14 [视频-长播] <|video_begin|> + 02:35 [广告-深度转化] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 05:08 [广告-深度转化] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 12:19 [广告-深度转化] <|ad_begin|> + 12:40 [广告-深度转化] <|ad_begin|> + 13:07 [广告-深度转化] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 15:24 [广告-深度转化] <|ad_begin|> + 18:04 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + 06:44 [广告-深度转化] <|ad_begin|> + 07:09 [广告-深度转化] <|ad_begin|> + 09:28 [广告-深度转化] <|ad_begin|> + 10:58 [广告-深度转化] <|ad_begin|> + 21:05 [广告-深度转化] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 06:38 [广告-深度转化] <|ad_begin|> + 08:41 [广告-深度转化] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 12:37 [广告-深度转化] <|ad_begin|> + 12:43 [广告-深度转化] <|ad_begin|> + 13:28 [广告-深度转化] <|ad_begin|> + 16:29 [广告-深度转化] <|ad_begin|> + 16:48 [广告-深度转化] <|ad_begin|> + 16:53 [广告-深度转化] <|ad_begin|> +【2026-01-28】 + 03:39 [广告-深度转化] <|ad_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:27 [视频-长播] <|video_begin|> + 04:31 [视频-长播] <|video_begin|> + 04:31 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:56 [视频-长播] <|video_begin|> + 11:58 [广告-深度转化] <|ad_begin|> + 12:09 [广告-深度转化] <|ad_begin|> + 13:02 [广告-深度转化] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 15:27 [广告-深度转化] <|ad_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 17:44 [广告-深度转化] <|ad_begin|> + 18:50 [广告-深度转化] <|ad_begin|> + 19:24 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:01 [广告-深度转化] <|ad_begin|> + 20:18 [广告-深度转化] <|ad_begin|> + 22:15 [广告-深度转化] <|ad_begin|> + 22:57 [视频-长播] <|video_begin|> + 22:57 [视频-关注/长播] <|video_begin|> +【2026-01-29】 + 01:01 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 12:28 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:12 [广告-点击] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 13:35 [广告-深度转化] <|ad_begin|> + 13:35 [广告-点击] <|ad_begin|> + 14:23 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:15 [广告-点击] <|ad_begin|> + 17:29 [广告-深度转化] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> + 17:43 [广告-点击] <|ad_begin|> + 17:56 [广告-点击] <|ad_begin|> + 18:01 [广告-点击] <|ad_begin|> + 18:25 [广告-点击] <|ad_begin|> + 18:25 [广告-点击] <|ad_begin|> + 18:32 [广告-点击] <|ad_begin|> + 18:37 [视频-长播] <|video_begin|> + 18:39 [视频-长播] <|video_begin|> + 18:39 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + 03:33 [广告-点击] <|ad_begin|> + 10:36 [广告-深度转化] <|ad_begin|> + 10:36 [广告-点击] <|ad_begin|> + 13:25 [广告-点击] <|ad_begin|> + 15:06 [广告-点击] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> + 15:19 [广告-点击] <|ad_begin|> + 15:22 [广告-点击] <|ad_begin|> + 15:23 [广告-点击] <|ad_begin|> + 15:42 [广告-深度转化] <|ad_begin|> + 15:42 [广告-点击] <|ad_begin|> + 16:12 [视频-转发/长播] <|video_begin|> + 16:45 [广告-深度转化] <|ad_begin|> + 16:45 [广告-点击] <|ad_begin|> + 16:49 [视频-长播] <|video_begin|> + 17:06 [广告-点击] <|ad_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:14 [视频-长播] <|video_begin|> + 17:56 [广告-点击] <|ad_begin|> + 17:58 [广告-点击] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> + 19:19 [视频-转发/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 19:41 [视频-长播] <|video_begin|> +【2026-01-31】 + 04:46 [广告-点击] <|ad_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 13:55 [广告-点击] <|ad_begin|> + 14:03 [广告-深度转化] <|ad_begin|> + 14:03 [广告-点击] <|ad_begin|> + 14:30 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + 04:07 [广告-点击] <|ad_begin|> + 05:54 [视频-长播] <|video_begin|> + 05:54 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 07:33 [广告-点击] <|ad_begin|> + 07:59 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:10 [视频-长播] <|video_begin|> + 09:59 [广告-点击] <|ad_begin|> + 11:44 [广告-点击] <|ad_begin|> + 13:06 [广告-点击] <|ad_begin|> + 13:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + 02:01 [广告-点击] <|ad_begin|> + 02:04 [广告-深度转化] <|ad_begin|> + 02:04 [广告-点击] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:09 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 02:19 [广告-点击] <|ad_begin|> + 02:20 [广告-点击] <|ad_begin|> + 02:23 [广告-点击] <|ad_begin|> + 02:26 [广告-点击] <|ad_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:50 [广告-点击] <|ad_begin|> + 07:46 [视频-长播] <|video_begin|> + 07:46 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 09:22 [广告-点击] <|ad_begin|> + 15:13 [广告-点击] <|ad_begin|> + 15:38 [广告-点击] <|ad_begin|> + 15:40 [广告-点击] <|ad_begin|> + 15:44 [广告-点击] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> + 16:14 [广告-点击] <|ad_begin|> + 16:21 [广告-点击] <|ad_begin|> + 16:22 [广告-点击] <|ad_begin|> + 16:41 [广告-点击] <|ad_begin|> + 23:25 [广告-点击] <|ad_begin|> +【2026-02-03】 + 15:26 [广告-点击] <|ad_begin|> +【2026-02-04】 + 09:15 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 15:27 [广告-点击] <|ad_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑和内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑（递进性准则）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请面向以下主题提取行为逻辑链： +主题：从泛化祈福到特定化、功能化的玄学消费演进 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化祈福到特定化、功能化的玄学消费演进", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/think<|im_end|> +<|im_start|>assistant + + +{"logic_chain":{"name":"从泛化祈福到特定化、功能化的玄学消费演进","events":[{"date":"2025-10-09","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：购买泛化的、带有普适性祝福寓意（天官赐福）的玄学商品，作为兴趣起点。"},{"date":"2025-11-10","action":"[商品-购买] <|prod_begin|>","logic":"兴趣强化与时效性认知：从通用款转向具有明确年份（2026马年）指向的同类商品，并带有“老师推荐”标签，表明用户开始接受特定圈层的引导，认知从“随意买”向“跟着买”演进。"},{"date":"2025-12-15","action":"[商品-购买] <|prod_begin|>","logic":"认知分化与深度探索：兴趣从汉地主流的“天官赐福”体系，首次转向具有明确地域和文化符号（布宫、藏粉）的藏传佛教体系，实现了文化圈层的跨越，是显著的认知增量。"},{"date":"2025-12-18","action":"[商品-点击] <|prod_begin|>","logic":"需求聚焦与功能收敛：在经历了泛化祈福和文化体系探索后，用户的兴趣收敛到具有明确“招财”功能的“五路貔貅”上。这标志着用户的需求从“保平安”的模糊概念，聚焦到了“求财”这一具体目标，完成了从发散到聚焦的认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-04-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-01】 + --:-- [直播-关注] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + 14:27 [广告-深度转化] <|ad_begin|> + 16:10 [广告-深度转化] <|ad_begin|> + 16:28 [广告-深度转化] <|ad_begin|> + 16:30 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 14:28 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:20 [广告-深度转化] <|ad_begin|> + 14:10 [广告-深度转化] <|ad_begin|> + 17:12 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [商品-点击] <|prod_begin|> + 15:20 [广告-深度转化] <|ad_begin|> + 17:01 [广告-深度转化] <|ad_begin|> + 17:09 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:20 [广告-深度转化] <|ad_begin|> + 13:20 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> + 18:38 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 02:01 [广告-深度转化] <|ad_begin|> + 15:43 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> + 15:44 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + --:-- [直播-关注] <|living_begin|> + 08:31 [广告-深度转化] <|ad_begin|> + 14:58 [广告-深度转化] <|ad_begin|> + 15:21 [广告-深度转化] <|ad_begin|> + 16:23 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:06 [广告-深度转化] <|ad_begin|> + 15:30 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + --:-- [直播-关注] <|living_begin|> + 04:21 [广告-深度转化] <|ad_begin|> + 15:36 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + --:-- [商品-点击] <|prod_begin|> + 13:16 [广告-深度转化] <|ad_begin|> + 15:23 [广告-深度转化] <|ad_begin|> + 16:37 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-28】 + 03:13 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-30】 + 05:25 [广告-深度转化] <|ad_begin|> + 09:24 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 16:35 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 03:24 [广告-深度转化] <|ad_begin|> + 03:56 [广告-深度转化] <|ad_begin|> + 06:00 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-01】 + 15:22 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> + 17:27 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 04:09 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 02:27 [广告-深度转化] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 15:55 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [商品-点击] <|prod_begin|> + 11:20 [广告-深度转化] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + 13:55 [广告-深度转化] <|ad_begin|> + 15:51 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 10:33 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:54 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 10:11 [广告-深度转化] <|ad_begin|> + 15:27 [广告-深度转化] <|ad_begin|> + 15:38 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 04:52 [广告-深度转化] <|ad_begin|> + 16:13 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [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|> +【2026-01-12】 + 12:34 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:38 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 04:28 [广告-深度转化] <|ad_begin|> + 13:50 [广告-深度转化] <|ad_begin|> + 14:05 [广告-深度转化] <|ad_begin|> + 14:55 [广告-深度转化] <|ad_begin|> + 17:56 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 04:31 [广告-深度转化] <|ad_begin|> + 14:18 [广告-点击] <|ad_begin|> + 14:37 [广告-点击] <|ad_begin|> + 14:48 [广告-深度转化] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 16:08 [广告-点击] <|ad_begin|> +【2026-01-17】 + 09:20 [广告-深度转化] <|ad_begin|> + 09:20 [广告-点击] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 14:58 [广告-点击] <|ad_begin|> + 15:46 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + 07:10 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:57 [广告-点击] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + 03:57 [广告-点击] <|ad_begin|> + 04:57 [广告-点击] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + 13:22 [广告-点击] <|ad_begin|> + 16:05 [广告-点击] <|ad_begin|> +【2026-01-21】 + 09:58 [广告-点击] <|ad_begin|> +【2026-01-22】 + 03:48 [广告-点击] <|ad_begin|> + 08:48 [广告-点击] <|ad_begin|> + 09:00 [广告-深度转化] <|ad_begin|> + 09:00 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:59 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:20 [广告-点击] <|ad_begin|> + 05:13 [广告-点击] <|ad_begin|> + 11:32 [广告-点击] <|ad_begin|> + 12:06 [广告-点击] <|ad_begin|> +【2026-01-24】 + 00:29 [广告-点击] <|ad_begin|> + 01:15 [广告-点击] <|ad_begin|> + 04:05 [广告-点击] <|ad_begin|> + 04:29 [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-购买] <|prod_begin|> + 13:21 [广告-点击] <|ad_begin|> + 15:26 [广告-点击] <|ad_begin|> +【2026-01-25】 + 01:46 [广告-点击] <|ad_begin|> + 01:48 [广告-点击] <|ad_begin|> + 08:35 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:07 [广告-点击] <|ad_begin|> + 17:04 [广告-点击] <|ad_begin|> +【2026-01-26】 + 03:31 [广告-点击] <|ad_begin|> + 06:42 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 16:34 [广告-点击] <|ad_begin|> +【2026-01-27】 + 15:58 [广告-点击] <|ad_begin|> +【2026-01-28】 + 03:27 [广告-点击] <|ad_begin|> + 03:34 [广告-点击] <|ad_begin|> + 03:35 [广告-点击] <|ad_begin|> + 08:55 [广告-点击] <|ad_begin|> + 16:04 [广告-深度转化] <|ad_begin|> + 16:04 [广告-点击] <|ad_begin|> +【2026-01-29】 + 02:16 [广告-点击] <|ad_begin|> + 02:30 [广告-点击] <|ad_begin|> + 02:45 [广告-点击] <|ad_begin|> + 03:04 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-30】 + 03:24 [广告-点击] <|ad_begin|> + 03:28 [广告-点击] <|ad_begin|> + 16:13 [广告-深度转化] <|ad_begin|> + 16:13 [广告-点击] <|ad_begin|> +【2026-01-31】 + 07:26 [视频-长播] <|video_begin|> + 07:26 [视频-长播] <|video_begin|> + 15:36 [视频-长播] <|video_begin|> + 15:36 [视频-长播] <|video_begin|> +【2026-02-01】 + 12:48 [视频-长播] <|video_begin|> + 13:00 [广告-点击] <|ad_begin|> + 13:07 [视频-长播] <|video_begin|> + 17:22 [广告-深度转化] <|ad_begin|> + 17:22 [广告-点击] <|ad_begin|> + 17:44 [视频-长播] <|video_begin|> +【2026-02-02】 + 10:27 [视频-长播] <|video_begin|> + 10:50 [广告-点击] <|ad_begin|> + 11:00 [广告-深度转化] <|ad_begin|> + 11:00 [广告-点击] <|ad_begin|> + 12:31 [广告-点击] <|ad_begin|> + 14:35 [广告-点击] <|ad_begin|> + 14:36 [视频-长播] <|video_begin|> + 14:36 [视频-长播] <|video_begin|> + 15:02 [广告-点击] <|ad_begin|> + 15:26 [视频-长播] <|video_begin|> + 15:41 [广告-点击] <|ad_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + 11:15 [视频-长播] <|video_begin|> + 11:28 [广告-点击] <|ad_begin|> + 11:50 [广告-深度转化] <|ad_begin|> + 11:50 [广告-点击] <|ad_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:30 [视频-长播] <|video_begin|> + 12:50 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 13:32 [广告-点击] <|ad_begin|> + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 15:27 [视频-长播] <|video_begin|> + 15:27 [视频-长播] <|video_begin|> + 15:27 [视频-长播] <|video_begin|> + 16:08 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 03:43 [广告-点击] <|ad_begin|> + 06:58 [视频-长播] <|video_begin|> + 06:58 [视频-长播] <|video_begin|> + 08:47 [广告-深度转化] <|ad_begin|> + 08:47 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:52 [广告-点击] <|ad_begin|> + 12:16 [视频-长播] <|video_begin|> + 12:16 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 12:20 [视频-长播] <|video_begin|> + 13:38 [视频-长播] <|video_begin|> + 13:38 [视频-长播] <|video_begin|> + 13:52 [视频-长播] <|video_begin|> + 14:04 [广告-点击] <|ad_begin|> + 14:12 [视频-长播] <|video_begin|> + 14:49 [视频-长播] <|video_begin|> + 14:49 [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|> + 15:07 [视频-长播] <|video_begin|> + 15:07 [视频-长播] <|video_begin|> + 15:23 [视频-长播] <|video_begin|> +【2026-02-05】 + 13:57 [广告-点击] <|ad_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:01 [视频-长播] <|video_begin|> + 15:12 [视频-长播] <|video_begin|> + 15:12 [视频-长播] <|video_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心提取逻辑 (递进性准则)： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 与交互 A 必须处于同一生活场景和主题类目下，并具备极强的需求补完关系。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请针对下列主题提取行为逻辑链： +主题：从被动健康信息触达到主动寻求解决方案的演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动健康信息触达到主动寻求解决方案的演化", + "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" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从被动健康信息触达到主动寻求解决方案的演化","events":[{"date":"2025-12-24","action":"[广告-深度转化] <|ad_begin|>","logic":"初始触发：被动接收到关于“健康管理”和“体检报告解读”的泛健康概念广告，激发了对自身健康状况的关注。"},{"date":"2026-01-05","action":"[商品-点击] <|prod_begin|>","logic":"行为转化与问题聚焦：从被动的、宽泛的健康管理概念，转化为主动点击针对“疣/瘊”这一具体皮肤问题的产品。这表明用户的问题意识已从模糊的“健康”收敛到特定的、需要解决的病症上，完成了从被动接收到主动寻求的认知跃迁。"}]}}<|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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+好的，我将以推荐分析师的视角，对这项任务进行推理分析。 + +我接到的任务是，基于用户的交互历史，挖掘出一条从泛化工具认知到精准化电路检测工具选购的行为逻辑链。这意味着我需要从用户大量的行为数据中，找到一个清晰的、由浅入深的兴趣演变过程，最终导向一个具体的购买决策。 + +首先，我快速浏览了用户的整体行为时间线，发现其兴趣点非常广泛，涵盖了美女直播、情感内容、小说广告、休闲游戏以及各种生活日用品等。在如此庞杂的兴趣中，要找到一条关于“电路检测工具”的线索，我必须聚焦于那些与工具、维修、DIY相关的交互点。 + +在时间线的后期，我注意到了一些关键信号。在2月3日，用户转发并长时间观看了一个关于厨房装修的视频 <|video_begin|>。这个视频内容非常具体，展示了专业技术人员使用电钻、螺丝刀等工具进行管道铺设。我认为，这很可能是用户对这类工具产生初步认知的起点。虽然他可能之前也见过这些工具，但这次“转发”和“长播”的强交互行为，表明他对这个场景下的专业操作产生了较高的兴趣，可能由此意识到了在家居维修中拥有合适工具的重要性。 + +有了这个起点，我继续向后追溯。在接下来的两天里，用户的兴趣似乎又回到了他更常关注的美女、社交和生活内容上。然而，到了2月5日，一个关键的视频 <|video_begin|> 再次出现。这个视频专门介绍了“家用强扭力测电笔”，详细讲解了它如何检测电路、解决故障，甚至还提到了它兼具螺丝刀功能。我推断，这是用户兴趣的一次重要收敛和深化。从之前对“电钻”这类泛化电动工具的认知，他现在开始聚焦于“测电笔”这一更具体、更具诊断功能的专业工具。这次长播行为表明，他正在主动学习和了解这类工具的具体用途和价值。 + +这个发现让我非常兴奋，因为一条清晰的“认知-深化”路径已经浮现。接下来，我需要寻找将这种认知转化为实际购买行动的证据。我将目光锁定在2月6日，也就是观看测电笔介绍视频的第二天。果然，在这一天的商品交互记录中，我找到了决定性的闭环行为。用户不仅点击了多款测电笔商品，如 <|prod_begin|> 和 <|prod_begin|>，最终还明确地购买了其中一款具备“高亮感应”、“智能”、“测断点”等高级功能的新款测电笔 <|prod_begin|>。 + +这一系列的购买行为，完美地印证了我的推测。用户的需求已经从“了解”升级为“拥有”，并且他的选择非常精准，不再是泛泛地浏览，而是直接购买了功能更强、更智能化的产品。这表明前两天的视频内容已经成功地教育了用户，让他明确了自己的需求，从而做出了高效的购买决策。 + +综合来看，这条行为链条非常完整且逻辑递进关系清晰。它始于一个偶然的装修视频，触发了对专业工具的泛化认知；随后通过一个专门介绍视频，将兴趣深化并聚焦到测电笔这一细分品类上；最终在短时间内迅速转化为对一款功能明确的智能测电笔的购买行为，形成了一个从认知到决策的完整闭环。因此，我倾向于认为，这条路径精准地反映了用户在该主题下的兴趣演进和最终的消费转化。 +{"logic_chain": {"name": "从泛化工具认知到精准化电路检测工具的选购闭环", "events": [{"date": "2026-02-03", "action": "[视频-转发/长播] <|video_begin|>", "logic": "问题触发：通过观看装修视频，接触到“电钻”等电工工具的使用场景，形成初步认知。"}, {"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "认知深化：兴趣从通用电动工具收敛至“测电笔”这一特定诊断工具，并开始了解其核心功能（检测电路、解决故障）。"}, {"date": "2026-02-06", "action": "[商品-购买] <|prod_begin|>", "logic": "决策闭环：从了解基础功能转向购买具备“感应”、“智能”、“测断点”等高级功能的具体商品，体现了基于知识获取后的精准决策和消费转化。"}]}}<|im_end|> + + +{"logic_chain":{"name":"从泛化祈福到特定化、功能化的玄学消费演进","events":[{"date":"2025-10-09","action":"[商品-购买] <|prod_begin|>","logic":"初始触发：购买泛化的、带有普适性祝福寓意（天官赐福）的玄学商品，作为兴趣起点。"},{"date":"2025-11-10","action":"[商品-购买] <|prod_begin|>","logic":"兴趣强化与时效性认知：从通用款转向具有明确年份（2026马年）指向的同类商品，并带有“老师推荐”标签，表明用户开始接受特定圈层的引导，认知从“随意买”向“跟着买”演进。"},{"date":"2025-12-15","action":"[商品-购买] <|prod_begin|>","logic":"认知分化与深度探索：兴趣从汉地主流的“天官赐福”体系，首次转向具有明确地域和文化符号（布宫、藏粉）的藏传佛教体系，实现了文化圈层的跨越，是显著的认知增量。"},{"date":"2025-12-18","action":"[商品-点击] <|prod_begin|>","logic":"需求聚焦与功能收敛：在经历了泛化祈福和文化体系探索后，用户的兴趣收敛到具有明确“招财”功能的“五路貔貅”上。这标志着用户的需求从“保平安”的模糊概念，聚焦到了“求财”这一具体目标，完成了从发散到聚焦的认知闭环。"}]}}<|im_end|> +{"logic_chain":{"name":"从被动健康信息触达到主动寻求解决方案的演化","events":[{"date":"2025-12-24","action":"[广告-深度转化] <|ad_begin|>","logic":"初始触发：被动接收到关于“健康管理”和“体检报告解读”的泛健康概念广告，激发了对自身健康状况的关注。"},{"date":"2026-01-05","action":"[商品-点击] <|prod_begin|>","logic":"行为转化与问题聚焦：从被动的、宽泛的健康管理概念，转化为主动点击针对“疣/瘊”这一具体皮肤问题的产品。这表明用户的问题意识已从模糊的“健康”收敛到特定的、需要解决的病症上，完成了从被动接收到主动寻求的认知跃迁。"}]}}<|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 04:50:53,039 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 04:50:53,040 >> Model config Qwen3Config { + "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 +} + +[INFO|2026-07-15 04:50:53] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 04:50:53] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 04:50:53,361 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-15 04:50:53,361 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 04:50:53,505 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-15 04:50:53,793 >> Generate config GenerationConfig { + "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] 2026-07-15 04:50:53,793 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-15 04:50:53] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 04:50:53] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 04:50:53] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 04:50:53] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 04:50:53] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 04:50:53,922 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 04:50:53,922 >> Num examples = 3,055 +[INFO|trainer.py:1471] 2026-07-15 04:50:53,922 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-15 04:50:53,922 >> Num update steps per epoch = 764 +[INFO|trainer.py:1473] 2026-07-15 04:50:53,922 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 04:50:53,922 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 04:50:53,922 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 04:50:53,922 >> Total optimization steps = 764 +[INFO|trainer.py:1479] 2026-07-15 04:50:53,922 >> Number of trainable parameters = 801,433,600 + 0%| | 0/764 [00:00> `use_return_dict` is deprecated! 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'grad_norm': '4.938', 'learning_rate': '6.454e-08', 'epoch': '0.7463'} + 75%|███████▍ | 570/764 [1:16:30<25:10, 7.79s/it] 75%|███████▍ | 571/764 [1:16:38<25:14, 7.85s/it] 75%|███████▍ | 572/764 [1:16:46<25:16, 7.90s/it] 75%|███████▌ | 573/764 [1:16:55<25:46, 8.10s/it] 75%|███████▌ | 574/764 [1:17:02<24:58, 7.89s/it] 75%|███████▌ | 575/764 [1:17:10<24:32, 7.79s/it] 75%|███████▌ | 576/764 [1:17:18<24:56, 7.96s/it] 76%|███████▌ | 577/764 [1:17:27<25:20, 8.13s/it] 76%|███████▌ | 578/764 [1:17:35<25:13, 8.14s/it] 76%|███████▌ | 579/764 [1:17:43<25:13, 8.18s/it] 76%|███████▌ | 580/764 [1:17:51<25:09, 8.20s/it] {'loss': '1.696', 'grad_norm': '4.594', 'learning_rate': '5.843e-08', 'epoch': '0.7594'} + 76%|███████▌ | 580/764 [1:17:51<25:09, 8.20s/it] 76%|███████▌ | 581/764 [1:17:59<24:40, 8.09s/it] 76%|███████▌ | 582/764 [1:18:07<24:49, 8.19s/it] 76%|███████▋ | 583/764 [1:18:15<24:14, 8.03s/it] 76%|███████▋ | 584/764 [1:18:24<24:36, 8.20s/it] 77%|███████▋ | 585/764 [1:18:33<25:08, 8.43s/it] 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[1:20:32<22:07, 8.09s/it] 79%|███████▊ | 601/764 [1:20:41<22:45, 8.38s/it] 79%|███████▉ | 602/764 [1:20:49<22:19, 8.27s/it] 79%|███████▉ | 603/764 [1:20:58<22:54, 8.54s/it] 79%|███████▉ | 604/764 [1:21:05<21:34, 8.09s/it] 79%|███████▉ | 605/764 [1:21:14<21:51, 8.25s/it] 79%|███████▉ | 606/764 [1:21:22<21:46, 8.27s/it] 79%|███████▉ | 607/764 [1:21:30<21:13, 8.11s/it] 80%|███████▉ | 608/764 [1:21:38<20:38, 7.94s/it] 80%|███████▉ | 609/764 [1:21:46<20:48, 8.05s/it] 80%|███████▉ | 610/764 [1:21:55<21:02, 8.20s/it] {'loss': '1.636', 'grad_norm': '4.062', 'learning_rate': '4.165e-08', 'epoch': '0.7987'} + 80%|███████▉ | 610/764 [1:21:55<21:02, 8.20s/it] 80%|███████▉ | 611/764 [1:22:03<21:15, 8.33s/it] 80%|████████ | 612/764 [1:22:11<21:00, 8.29s/it] 80%|████████ | 613/764 [1:22:19<20:16, 8.06s/it] 80%|████████ | 614/764 [1:22:27<20:09, 8.06s/it] 80%|████████ | 615/764 [1:22:35<20:08, 8.11s/it] 81%|████████ | 616/764 [1:22:44<20:24, 8.28s/it] 81%|████████ | 617/764 [1:22:53<20:45, 8.47s/it] 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{'loss': '1.707', 'grad_norm': '5.25', 'learning_rate': '1.285e-08', 'epoch': '0.8903'} + 89%|████████▉ | 680/764 [1:31:24<11:39, 8.33s/it] 89%|████████▉ | 681/764 [1:31:33<11:45, 8.51s/it] 89%|████████▉ | 682/764 [1:31:42<11:58, 8.77s/it] 89%|████████▉ | 683/764 [1:31:51<11:42, 8.67s/it] 90%|████████▉ | 684/764 [1:31:58<11:05, 8.32s/it] 90%|████████▉ | 685/764 [1:32:07<10:59, 8.35s/it] 90%|████████▉ | 686/764 [1:32:15<10:45, 8.27s/it] 90%|████████▉ | 687/764 [1:32:23<10:29, 8.18s/it] 90%|█████████ | 688/764 [1:32:31<10:17, 8.12s/it] 90%|█████████ | 689/764 [1:32:40<10:32, 8.44s/it] 90%|█████████ | 690/764 [1:32:48<10:09, 8.24s/it] {'loss': '1.7', 'grad_norm': '4.875', 'learning_rate': '1.003e-08', 'epoch': '0.9034'} + 90%|█████████ | 690/764 [1:32:48<10:09, 8.24s/it] 90%|█████████ | 691/764 [1:32:55<09:47, 8.04s/it] 91%|█████████ | 692/764 [1:33:04<09:50, 8.20s/it] 91%|█████████ | 693/764 [1:33:12<09:46, 8.26s/it] 91%|█████████ | 694/764 [1:33:21<09:37, 8.25s/it] 91%|█████████ | 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Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '6161', 'train_samples_per_second': '0.496', 'train_steps_per_second': '0.124', 'train_loss': '1.71', 'epoch': '1'} + 100%|██████████| 764/764 [1:42:40<00:00, 7.57s/it] 100%|██████████| 764/764 [1:42:40<00:00, 8.06s/it] +[INFO|trainer.py:3815] 2026-07-15 06:33:34,795 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10 +[INFO|configuration_utils.py:533] 2026-07-15 06:33:34,797 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/config.json +[INFO|configuration_utils.py:816] 2026-07-15 06:33:34,797 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 06:33:35,747 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 06:33:35,769 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 342961094GF + train_loss = 1.7103 + train_runtime = 1:42:40.87 + train_samples_per_second = 0.496 + train_steps_per_second = 0.124 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v41_v29_baseline_general_replay_lr4e7_ep10/training_loss.png +[WARNING|2026-07-15 06:33:35] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 06:33:35] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 06:33:35,872 >> 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..4f80c43 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.68251671698516e+17, + "train_loss": 1.7103283592543677, + "train_runtime": 6160.8721, + "train_samples_per_second": 0.496, + "train_steps_per_second": 0.124 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..2e5d563 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,77 @@ +{"current_steps": 10, "total_steps": 764, "loss": 1.7698013305664062, "lr": 1.5652173913043477e-07, "epoch": 0.01309328968903437, "percentage": 1.31, "elapsed_time": "0:01:22", "remaining_time": "1:43:06"} +{"current_steps": 20, "total_steps": 764, "loss": 1.8119827270507813, "lr": 3.304347826086956e-07, "epoch": 0.02618657937806874, "percentage": 2.62, "elapsed_time": 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