commit fd8c30c1a9a29616c47e6aa7b8699a42976c0b75 Author: ModelHub XC Date: Sat Jul 18 06:57:11 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: lldois/v23_scratch_clean_cot_guard_lr3e6_ep5 Source: Original Platform 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..4a36162 --- /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: v23_scratch_clean_cot_guard_lr3e6_ep5 + results: [] +--- + + + +# v23_scratch_clean_cot_guard_lr3e6_ep5 + +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_scratch_clean_cot_guard_ep5 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: 3e-06 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607232 +- 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: 0.02 +- num_epochs: 5 + +### 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..dd1f335 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 5.0, + "total_flos": 1.5488981277124608e+18, + "train_loss": 1.8179572771795047, + "train_runtime": 26174.2071, + "train_samples_per_second": 0.492, + "train_steps_per_second": 0.123 +} \ 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..669ce68 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,60 @@ +{ + "created_at": "2026-07-10 01:00:49", + "run": { + "name": "v23_scratch_clean_cot_guard_lr3e6_ep5", + "dataset": "scratch_clean_cot_guard_ep5", + "model_path": "OpenOneRec/OneReason-0.8B-pretrain-competition", + "lr": "3.0e-6", + "epochs": 5, + "warmup": 0.02, + "scheduler": "cosine", + "seed": 202607232, + "note": "Scratch SFT from official OneReason. More conservative 5-epoch CoT run with lower LR, heavier item replay, and lighter rec augmentation. Tests whether 3-5 epoch training helps when update size is controlled.", + "config": "/home/ll/llm4rec/experiments/configs/v23_scratch_clean_cot_guard_lr3e6_ep5.yaml" + }, + "dataset_manifest": { + "name": "scratch_clean_cot_guard_ep5", + "path": "/home/ll/llm4rec/experiments/data/scratch_clean_cot_guard_ep5.jsonl", + "records": 60765, + "groups": { + "rec": 27204, + "item": 27175, + "user": 6386 + }, + "variants": { + "raw": 32480, + "item_compact_cot": 5597, + "rec_no_think_direct_final": 3200, + "rec_cot_pattern_clean": 4800, + "item_no_think_direct_final": 5610, + "item_short_think": 5584, + "user_strict_array": 1588, + "user_extra_no_think_logic": 602, + "user_strict_logic": 1304 + }, + "sha256": "805b812a7cf3f9d86b8067d1d19b7fc116fb6739c75ab6aa04c16a4a13bb1419", + "seed": 202607112 + }, + "dataset_recipe": "Scratch official-OneReason SFT: original CoT all + 4800 cleaned rec CoT + 3200 rec /no_think direct-final + strict dual user aug + 3x item-style replay. Lower LR and 5 epochs test longer training without v7 final-only bias.", + "cot_policy": "Preserve /think reasoning supervision and do not use v7_final_only as a CoT training base. For /no_think prompts, train pure final answers without generated tags. This is route-specific behavior, not global CoT removal.", + "raw_counts": { + "rec": 19204, + "item": 10384, + "user": 2892 + }, + "eval_observations": { + "v07": "best local score so far: total=0.8978, eval_time≈47.3min; fast final outputs likely help.", + "v15": "best CoT-preserving score so far: total=0.8778, eval_time≈70.1min; logs show repeated tokens, JSON shell errors, prompt leakage, and verbose /no_think outputs.", + "v19": "best CoT-native continuation so far: total=0.8855, eval_time≈48.1min; user1 and rec4 improved but world dropped.", + "v20": "v7 final-only continuation with CoT restore failed as a CoT route: total=0.8527, item fell to 0.1840; do not use v7 as future CoT base.", + "v16": "CoT pattern rewrite failed: total=0.7912; logs show malformed user JSON and fragmented recommendation reasoning.", + "v18": "low-LR mixed replay from v12 failed: total=0.8340; item/world dropped and rec outputs mixed text/itemic/think tags." + }, + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "a03ddf5a479db437247aed4b8000b25cb5021988029220bd818133123951aa00", + "deadline": "2026-07-10 09:00:00 +0800", + "reproduce": { + "prepare_command": "EXPERIMENT_PREPARE_ONLY=1 python3 experiments/run_experiments.py", + "train_command": "CUDA_VISIBLE_DEVICES= bash -lc 'source /home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/activate && llamafactory-cli train /home/ll/llm4rec/experiments/configs/v23_scratch_clean_cot_guard_lr3e6_ep5.yaml'" + } +} \ 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..bcc8490 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af3c406546c56c25c6a135da063d5851d85acf0a7f0284b53354ac6c097f8a6e +size 1602902832 diff --git a/runs/Jul10_01-16-59_ubuntu-System-Product-Name/events.out.tfevents.1783617419.ubuntu-System-Product-Name.2052816.0 b/runs/Jul10_01-16-59_ubuntu-System-Product-Name/events.out.tfevents.1783617419.ubuntu-System-Product-Name.2052816.0 new file mode 100644 index 0000000..90714e7 --- /dev/null +++ b/runs/Jul10_01-16-59_ubuntu-System-Product-Name/events.out.tfevents.1783617419.ubuntu-System-Product-Name.2052816.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96257b5a9f5b9691bf7d5b0ed7fc27704f29fe44bee633dc738c8cb17f8288bd +size 142093 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..4d254e4 --- /dev/null +++ b/train.log @@ -0,0 +1,2939 @@ +/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|2026-07-10 01:16:22] 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] 2026-07-10 01:16:23,339 >> 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] 2026-07-10 01:16:23,341 >> 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] 2026-07-10 01:16:27,873 >> 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] 2026-07-10 01:16:27,874 >> 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] 2026-07-10 01:16:28,361 >> 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] 2026-07-10 01:16:28,362 >> 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|2026-07-10 01:16:30] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/scratch_clean_cot_guard_ep5.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: 11551 examples [00:00, 113135.35 examples/s] Generating train split: 27039 examples [00:00, 130877.44 examples/s] Generating train split: 43970 examples [00:00, 142599.03 examples/s] Generating train split: 60765 examples [00:00, 145752.66 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/60765 [00:00user +【用户交互历史】： +【2024-09-03】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-23】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-18】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-09】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-12】 + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-05】 + --:-- [直播-关注] <|living_begin|> +【2025-03-07】 + --:-- [直播-关注] <|living_begin|> +【2025-03-08】 + --:-- [直播-关注] <|living_begin|> +【2025-03-11】 + --:-- [直播-关注] <|living_begin|> +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> +【2025-03-15】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-28】 + --:-- [直播-关注] <|living_begin|> +【2025-03-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> +【2025-04-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-07】 + --:-- [直播-关注] <|living_begin|> +【2025-04-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-03】 + --:-- [直播-关注] <|living_begin|> +【2025-05-08】 + --:-- [直播-关注] <|living_begin|> +【2025-05-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-15】 + --:-- [直播-关注] <|living_begin|> +【2025-05-25】 + --:-- [直播-关注] <|living_begin|> +【2025-05-29】 + --:-- [直播-关注] <|living_begin|> +【2025-06-02】 + --:-- [直播-关注] <|living_begin|> +【2025-06-03】 + --:-- [直播-关注] <|living_begin|> +【2025-06-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-09】 + --:-- [直播-关注] <|living_begin|> +【2025-06-15】 + --:-- [直播-关注] <|living_begin|> +【2025-06-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-24】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-29】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-08-26】 + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-27】 + --:-- [直播-关注] <|living_begin|> +【2025-10-11】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-22】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-18】 + --:-- [直播-关注] <|living_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + 02:35 [广告-深度转化] <|ad_begin|> + 08:19 [广告-深度转化] <|ad_begin|> + 08:21 [广告-深度转化] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> + 08:29 [广告-深度转化] <|ad_begin|> + 08:38 [广告-深度转化] <|ad_begin|> + 08:51 [广告-深度转化] <|ad_begin|> + 09:00 [广告-深度转化] <|ad_begin|> + 09:15 [广告-深度转化] <|ad_begin|> + 23:48 [广告-深度转化] <|ad_begin|> + 23:52 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + 01:24 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 12:32 [广告-深度转化] <|ad_begin|> + 23:23 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + 12:34 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + 06:11 [广告-深度转化] <|ad_begin|> + 06:18 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 09:02 [广告-深度转化] <|ad_begin|> + 22:55 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 09:30 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:07 [广告-深度转化] <|ad_begin|> + 02:42 [广告-深度转化] <|ad_begin|> + 12:51 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + --:-- [直播-关注] <|living_begin|> + 07:47 [广告-深度转化] <|ad_begin|> + 15:34 [广告-深度转化] <|ad_begin|> +【2025-12-24】 + 07:05 [广告-深度转化] <|ad_begin|> + 07:20 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + 02:27 [广告-深度转化] <|ad_begin|> + 02:28 [广告-深度转化] <|ad_begin|> + 03:20 [广告-深度转化] <|ad_begin|> + 07:09 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + 03:12 [广告-深度转化] <|ad_begin|> + 10:06 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 00:40 [广告-深度转化] <|ad_begin|> + 00:41 [广告-深度转化] <|ad_begin|> + 05:13 [广告-深度转化] <|ad_begin|> + 10:19 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 00:49 [广告-深度转化] <|ad_begin|> + 04:09 [广告-深度转化] <|ad_begin|> + 23:07 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 03:28 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 07:56 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + 03:36 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 02:57 [广告-深度转化] <|ad_begin|> + 03:07 [广告-深度转化] <|ad_begin|> + 12:25 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 02:12 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 04:37 [广告-深度转化] <|ad_begin|> + 04:40 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + 04:27 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 08:00 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 07:33 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + --:-- [商品-购买] <|prod_begin|> + 08:40 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-16】 + 02:27 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-23】 + 22:26 [视频-长播] <|video_begin|> + 22:26 [视频-长播] <|video_begin|> + 22:35 [视频-长播] <|video_begin|> + 23:16 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 02:02 [广告-深度转化] <|ad_begin|> + 07:07 [视频-长播] <|video_begin|> + 10:25 [视频-长播] <|video_begin|> + 10:35 [视频-长播] <|video_begin|> + 10:41 [视频-长播] <|video_begin|> +【2026-01-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 04:50 [视频-长播] <|video_begin|> + 07:17 [视频-长播] <|video_begin|> + 10:37 [视频-长播] <|video_begin|> + 10:37 [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|> + 21:43 [视频-长播] <|video_begin|> +【2026-01-26】 + 00:31 [视频-长播] <|video_begin|> + 04:50 [视频-收藏/长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> +【2026-01-27】 + 21:35 [视频-长播] <|video_begin|> + 23:08 [视频-长播] <|video_begin|> +【2026-01-28】 + 00:43 [视频-长播] <|video_begin|> + 14:02 [广告-深度转化] <|ad_begin|> + 22:34 [广告-深度转化] <|ad_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|> + 12:54 [视频-长播] <|video_begin|> + 16:51 [视频-长播] <|video_begin|> + 16:51 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:48 [视频-长播] <|video_begin|> + 06:25 [视频-长播] <|video_begin|> + 12:28 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + 00:33 [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-购买] <|prod_begin|> + 07:01 [视频-长播] <|video_begin|> + 12:49 [视频-长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> + 21:14 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:48 [视频-长播] <|video_begin|> + 03:13 [广告-深度转化] <|ad_begin|> + 09:34 [视频-长播] <|video_begin|> + 10:16 [视频-长播] <|video_begin|> + 12:43 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:33 [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-购买] <|prod_begin|> + 05:20 [视频-长播] <|video_begin|> + 05:20 [视频-长播] <|video_begin|> + 05:27 [视频-长播] <|video_begin|> + 07:20 [视频-长播] <|video_begin|> + 09:44 [广告-点击] <|ad_begin|> + 10:05 [广告-点击] <|ad_begin|> + 10:08 [广告-点击] <|ad_begin|> + 10:16 [广告-点击] <|ad_begin|> + 10:26 [广告-点击] <|ad_begin|> + 12:44 [视频-长播] <|video_begin|> + 12:58 [广告-点击] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> + 13:22 [广告-点击] <|ad_begin|> + 13:23 [广告-点击] <|ad_begin|> + 22:16 [视频-长播] <|video_begin|> + 22:20 [广告-点击] <|ad_begin|> + 22:22 [广告-点击] <|ad_begin|> + 22:24 [广告-点击] <|ad_begin|> + 22:26 [广告-点击] <|ad_begin|> + 22:27 [广告-点击] <|ad_begin|> + 22:29 [广告-点击] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> + 22:38 [广告-点击] <|ad_begin|> + 22:39 [广告-点击] <|ad_begin|> + 22:43 [广告-点击] <|ad_begin|> + 22:44 [广告-点击] <|ad_begin|> + 22:45 [广告-点击] <|ad_begin|> + 22:46 [广告-点击] <|ad_begin|> + 23:03 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + 02:16 [广告-点击] <|ad_begin|> + 02:20 [广告-点击] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 02:26 [视频-长播] <|video_begin|> + 02:37 [广告-点击] <|ad_begin|> + 02:49 [广告-点击] <|ad_begin|> + 03:02 [广告-深度转化] <|ad_begin|> + 03:02 [广告-点击] <|ad_begin|> + 03:03 [广告-点击] <|ad_begin|> + 03:04 [广告-点击] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 07:33 [广告-点击] <|ad_begin|> + 07:57 [广告-深度转化] <|ad_begin|> + 07:57 [广告-点击] <|ad_begin|> + 08:39 [广告-点击] <|ad_begin|> + 11:04 [广告-点击] <|ad_begin|> + 11:05 [广告-点击] <|ad_begin|> + 11:19 [广告-深度转化] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 11:25 [广告-点击] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> + 13:00 [广告-点击] <|ad_begin|> + 20:54 [广告-点击] <|ad_begin|> + 20:56 [广告-点击] <|ad_begin|> + 21:01 [广告-点击] <|ad_begin|> + 21:02 [广告-点击] <|ad_begin|> + 21:05 [广告-点击] <|ad_begin|> + 21:07 [广告-深度转化] <|ad_begin|> + 21:07 [广告-点击] <|ad_begin|> + 23:11 [广告-点击] <|ad_begin|> +【2026-02-05】 + 01:35 [广告-深度转化] <|ad_begin|> + 01:35 [广告-点击] <|ad_begin|> + 01:37 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:10 [视频-长播] <|video_begin|> + 03:10 [视频-长播] <|video_begin|> + 03:24 [广告-点击] <|ad_begin|> + 03:32 [广告-点击] <|ad_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 06:24 [视频-长播] <|video_begin|> + 06:24 [视频-长播] <|video_begin|> + 06:34 [广告-点击] <|ad_begin|> + 06:35 [广告-深度转化] <|ad_begin|> + 06:35 [广告-点击] <|ad_begin|> + 06:39 [视频-长播] <|video_begin|> + 06:56 [视频-长播] <|video_begin|> + 08:36 [广告-点击] <|ad_begin|> + 08:41 [广告-点击] <|ad_begin|> + 09:06 [视频-收藏/长播] <|video_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 09:08 [广告-点击] <|ad_begin|> + 09:11 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 09:38 [广告-点击] <|ad_begin|> + 13:02 [广告-深度转化] <|ad_begin|> + 13:02 [广告-点击] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> + 13:23 [广告-深度转化] <|ad_begin|> + 13:23 [广告-点击] <|ad_begin|> + 13:23 [广告-深度转化] <|ad_begin|> + 13:23 [广告-点击] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 13:31 [广告-点击] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> + 13:41 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + +角色任务：作为一名极端严苛的用户行为数据挖掘与数据格式化专家，请根据上述用户交互历史，围绕指定主题，精准提取出所有相关的历史交互记录。 + +主题：从泛化养生到聚焦特定食材的认知收敛 + +输出格式要求：请仅以包含 SID 的 JSON 数组形式返回结果，切勿输出任何额外的解释说明或无关字符。/no_think<|im_end|> +<|im_start|>assistant + + +["<|prod_begin|>", "<|prod_begin|>", "<|video_begin|>", "<|prod_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-02-16】 + --:-- [直播-关注] <|living_begin|> +【2025-03-18】 + --:-- [直播-关注] <|living_begin|> +【2025-05-26】 + --:-- [直播-关注] <|living_begin|> +【2025-06-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-06-14】 + --:-- [直播-关注] <|living_begin|> +【2025-06-15】 + --:-- [直播-关注] <|living_begin|> +【2025-06-17】 + --:-- [直播-关注] <|living_begin|> +【2025-06-23】 + --:-- [直播-关注] <|living_begin|> +【2025-06-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-06-29】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> +【2025-08-27】 + --:-- [直播-关注] <|living_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-21】 + --:-- [直播-关注] <|living_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-30】 + --:-- [直播-关注] <|living_begin|> +【2025-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [直播-关注] <|living_begin|> +【2025-10-10】 + --:-- [直播-关注] <|living_begin|> +【2025-10-13】 + --:-- [直播-关注] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + 10:38 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + 03:45 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + 02:30 [广告-深度转化] <|ad_begin|> + 02:30 [广告-点击] <|ad_begin|> + 06:16 [广告-深度转化] <|ad_begin|> + 06:16 [广告-点击] <|ad_begin|> + 06:18 [广告-深度转化] <|ad_begin|> + 06:18 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-31】 + 05:50 [广告-深度转化] <|ad_begin|> + 05:50 [广告-点击] <|ad_begin|> + 09:06 [广告-深度转化] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 07:51 [广告-深度转化] <|ad_begin|> + 07:51 [广告-点击] <|ad_begin|> + 09:18 [广告-深度转化] <|ad_begin|> + 09:18 [广告-点击] <|ad_begin|> + 09:20 [广告-深度转化] <|ad_begin|> + 09:20 [广告-点击] <|ad_begin|> + 09:21 [广告-深度转化] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 09:26 [广告-深度转化] <|ad_begin|> + 09:26 [广告-点击] <|ad_begin|> + 09:31 [广告-深度转化] <|ad_begin|> + 09:31 [广告-点击] <|ad_begin|> +【2026-01-03】 + 05:08 [广告-深度转化] <|ad_begin|> + 05:08 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + 09:20 [广告-深度转化] <|ad_begin|> + 09:20 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> +【2026-01-10】 + 03:28 [广告-深度转化] <|ad_begin|> + 03:28 [广告-点击] <|ad_begin|> + 03:28 [广告-深度转化] <|ad_begin|> + 03:28 [广告-点击] <|ad_begin|> + 03:29 [广告-深度转化] <|ad_begin|> + 03:29 [广告-点击] <|ad_begin|> + 03:29 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + 14:54 [广告-深度转化] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> + 14:54 [广告-深度转化] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> +【2026-01-15】 + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-17】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + 18:28 [广告-深度转化] <|ad_begin|> + 18:28 [广告-点击] <|ad_begin|> + 18:29 [广告-深度转化] <|ad_begin|> + 18:29 [广告-点击] <|ad_begin|> + 18:29 [广告-深度转化] <|ad_begin|> + 18:29 [广告-点击] <|ad_begin|> + 18:29 [广告-深度转化] <|ad_begin|> + 18:29 [广告-点击] <|ad_begin|> + 18:55 [广告-深度转化] <|ad_begin|> + 18:55 [广告-点击] <|ad_begin|> + 18:55 [广告-点击] <|ad_begin|> + 18:55 [广告-深度转化] <|ad_begin|> + 18:55 [广告-点击] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> + 18:58 [广告-深度转化] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> + 18:58 [广告-深度转化] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> + 18:58 [广告-深度转化] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> + 18:58 [广告-深度转化] <|ad_begin|> + 18:58 [广告-点击] <|ad_begin|> + 19:19 [广告-深度转化] <|ad_begin|> + 19:19 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> +【2026-01-22】 + 23:05 [广告-深度转化] <|ad_begin|> + 23:05 [广告-点击] <|ad_begin|> + 23:22 [广告-点击] <|ad_begin|> + 23:23 [广告-深度转化] <|ad_begin|> + 23:23 [广告-点击] <|ad_begin|> + 23:27 [广告-深度转化] <|ad_begin|> + 23:27 [广告-点击] <|ad_begin|> + 23:27 [广告-深度转化] <|ad_begin|> + 23:27 [广告-点击] <|ad_begin|> + 23:28 [广告-深度转化] <|ad_begin|> + 23:28 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> +【2026-01-24】 + 05:48 [视频-转发/长播] <|video_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 06:17 [广告-深度转化] <|ad_begin|> + 06:17 [广告-点击] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-26】 + 11:18 [视频-关注/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 14:42 [广告-深度转化] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> + 14:42 [广告-深度转化] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> + 14:42 [广告-深度转化] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> + 22:25 [视频-转发/长播] <|video_begin|> + 22:26 [广告-点击] <|ad_begin|> + 22:26 [广告-深度转化] <|ad_begin|> + 22:26 [广告-点击] <|ad_begin|> + 22:29 [广告-深度转化] <|ad_begin|> + 22:29 [广告-点击] <|ad_begin|> + 22:30 [广告-深度转化] <|ad_begin|> + 22:30 [广告-点击] <|ad_begin|> + 22:31 [广告-深度转化] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> + 22:31 [广告-点击] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> + 22:32 [广告-深度转化] <|ad_begin|> + 22:32 [广告-点击] <|ad_begin|> + 22:33 [广告-深度转化] <|ad_begin|> + 22:33 [广告-点击] <|ad_begin|> + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> + 22:34 [广告-深度转化] <|ad_begin|> + 22:34 [广告-点击] <|ad_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> + 22:36 [广告-点击] <|ad_begin|> + 22:36 [广告-深度转化] <|ad_begin|> + 22:36 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [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|> +【2026-01-29】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-30】 + 10:27 [视频-转发/长播] <|video_begin|> + 10:28 [视频-转发/长播] <|video_begin|> +【2026-01-31】 + 08:53 [广告-深度转化] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + --:-- [商品-购买] <|prod_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-04】 + 03:44 [视频-长播] <|video_begin|> + 03:44 [视频-长播] <|video_begin|> + 03:44 [视频-长播] <|video_begin|> + 03:45 [视频-长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 10:01 [视频-长播] <|video_begin|> + 10:01 [视频-长播] <|video_begin|> + 10:02 [视频-长播] <|video_begin|> + 10:02 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:06 [视频-长播] <|video_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:12 [视频-长播] <|video_begin|> + 12:12 [视频-长播] <|video_begin|> + 12:12 [视频-长播] <|video_begin|> + 12:12 [视频-长播] <|video_begin|> + 14:07 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-05】 + 00:48 [视频-长播] <|video_begin|> + 00:48 [视频-转发/长播] <|video_begin|> + 00:48 [视频-长播] <|video_begin|> + 00:48 [视频-长播] <|video_begin|> + 00:48 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 02:46 [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|> + 08:08 [视频-长播] <|video_begin|> + 09:15 [视频-长播] <|video_begin|> + 11:50 [视频-长播] <|video_begin|> + 14:27 [视频-长播] <|video_begin|> + 14:27 [视频-点赞/长播] <|video_begin|> + 15:12 [视频-长播] <|video_begin|> + 15:16 [视频-转发/长播] <|video_begin|> + 15:17 [视频-长播] <|video_begin|> + 15:17 [视频-长播] <|video_begin|> + 15:18 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_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）： 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 +核心提取逻辑 (递进性准则)： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 所延伸出的需求，严禁提取任何品类平级、内容重复或仅由天气/促销触发的并列凑单行为。 +1.场景驱动协同：交互 B 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +2.兴趣因果递进：交互 B 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。 +请围绕以下主题提取行为逻辑链： +主题：从被动接收到主动探索：手机性能焦虑的解决路径 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收到主动探索：手机性能焦虑的解决路径", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从被动接收到主动探索：手机性能焦虑的解决路径", "events": [{"date": "2026-01-18", "action": "[广告-点击] <|ad_begin|>", "logic": "问题触发：首次通过广告被动接收到“手机性能优化”和“内存不足”的信息，问题意识被初步唤醒。"}, {"date": "2026-01-22", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣强化：再次点击相似主题的广告，表明对“手机垃圾清理”这一解决方案的兴趣得到确认和加强。"}, {"date": "2026-02-04", "action": "[视频-长播] <|video_begin|>", "logic": "认知深化与路径修正：从被动点击广告转向主动观看长视频。用户不再满足于一键清理的模糊概念，而是开始探索更具体的应用层级操作（如微信权限管理），这代表了从“系统性能”的泛焦虑，收敛到对“特定App功能”的深度探究，是典型的认知增量和行为模式升级。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-03-12】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-19】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-10】 + --:-- [商品-购买] <|prod_begin|> +【2023-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-18】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-01-18】 + --:-- [直播-关注] <|living_begin|> +【2025-01-19】 + --:-- [直播-关注] <|living_begin|> +【2025-01-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-01-22】 + --:-- [直播-关注] <|living_begin|> +【2025-01-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-01-25】 + --:-- [直播-关注] <|living_begin|> +【2025-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-02-06】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-02-18】 + --:-- [直播-关注] <|living_begin|> +【2025-02-19】 + --:-- [直播-关注] <|living_begin|> +【2025-02-23】 + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [直播-关注] <|living_begin|> +【2025-04-09】 + --:-- [直播-关注] <|living_begin|> +【2025-04-13】 + --:-- [直播-关注] <|living_begin|> +【2025-04-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-04-20】 + --:-- [直播-关注] <|living_begin|> +【2025-04-21】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [直播-关注] <|living_begin|> +【2025-04-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-29】 + --:-- [直播-关注] <|living_begin|> +【2025-05-15】 + --:-- [直播-关注] <|living_begin|> +【2025-05-16】 + --:-- [直播-关注] <|living_begin|> +【2025-06-10】 + --:-- [直播-关注] <|living_begin|> +【2025-06-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-04】 + --:-- [直播-关注] <|living_begin|> +【2025-07-06】 + --:-- [直播-关注] <|living_begin|> +【2025-07-20】 + --:-- [直播-关注] <|living_begin|> +【2025-07-23】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-30】 + --:-- [直播-关注] <|living_begin|> +【2025-08-06】 + --:-- [直播-关注] <|living_begin|> +【2025-08-10】 + --:-- [直播-关注] <|living_begin|> +【2025-08-18】 + --:-- [直播-关注] <|living_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-04】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-06】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-02】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-12】 + --:-- [商品-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|> +【2025-12-15】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + 02:33 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-20】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-21】 + 12:40 [广告-点击] <|ad_begin|> +【2025-12-22】 + 02:45 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:56 [广告-点击] <|ad_begin|> + 06:24 [广告-点击] <|ad_begin|> + 10:11 [广告-点击] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 13:38 [广告-点击] <|ad_begin|> + 14:16 [广告-点击] <|ad_begin|> +【2025-12-23】 + 02:39 [广告-点击] <|ad_begin|> + 03:53 [广告-点击] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 04:39 [广告-点击] <|ad_begin|> +【2025-12-24】 + 06:24 [广告-点击] <|ad_begin|> +【2025-12-26】 + 04:54 [广告-点击] <|ad_begin|> + 05:40 [广告-点击] <|ad_begin|> + 05:40 [广告-点击] <|ad_begin|> + 11:59 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:58 [广告-深度转化] <|ad_begin|> + 08:58 [广告-点击] <|ad_begin|> +【2026-01-02】 + 12:45 [广告-点击] <|ad_begin|> +【2026-01-03】 + 10:40 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-07】 + 03:22 [广告-点击] <|ad_begin|> + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> +【2026-01-10】 + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:44 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [商品-点击] <|prod_begin|> + 08:27 [广告-点击] <|ad_begin|> + 08:38 [广告-点击] <|ad_begin|> + 09:09 [广告-深度转化] <|ad_begin|> + 09:09 [广告-点击] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> +【2026-01-12】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + 07:02 [广告-深度转化] <|ad_begin|> + 07:02 [广告-点击] <|ad_begin|> +【2026-01-15】 + 05:27 [广告-点击] <|ad_begin|> + 05:45 [广告-深度转化] <|ad_begin|> + 05:45 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + 04:17 [广告-深度转化] <|ad_begin|> + 04:17 [广告-点击] <|ad_begin|> + 13:01 [广告-点击] <|ad_begin|> + 23:57 [广告-点击] <|ad_begin|> + 23:58 [广告-点击] <|ad_begin|> +【2026-01-18】 + 01:07 [广告-点击] <|ad_begin|> + 01:17 [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|> + 14:00 [广告-点击] <|ad_begin|> +【2026-01-20】 + 07:08 [广告-点击] <|ad_begin|> + 07:52 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [商品-点击] <|prod_begin|> + 06:37 [广告-点击] <|ad_begin|> + 06:38 [广告-点击] <|ad_begin|> + 12:08 [广告-深度转化] <|ad_begin|> + 12:08 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + 10:03 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-23】 + 09:22 [广告-点击] <|ad_begin|> + 13:35 [广告-点击] <|ad_begin|> +【2026-01-24】 + --:-- [商品-点击] <|prod_begin|> + 04:20 [广告-点击] <|ad_begin|> + 08:34 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-25】 + 00:23 [广告-深度转化] <|ad_begin|> + 00:23 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:59 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-26】 + 03:53 [视频-关注/点赞/长播] <|video_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-28】 + 14:03 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 03:40 [广告-点击] <|ad_begin|> +【2026-02-02】 + 05:52 [视频-关注/点赞/长播] <|video_begin|> + 05:53 [广告-点击] <|ad_begin|> + 05:55 [广告-点击] <|ad_begin|> + 05:57 [广告-点击] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 07:30 [广告-点击] <|ad_begin|> + 07:47 [广告-点击] <|ad_begin|> + 08:53 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 09:04 [视频-长播] <|video_begin|> + 09:07 [视频-长播] <|video_begin|> + 09:17 [视频-长播] <|video_begin|> + 09:20 [视频-长播] <|video_begin|> + 09:20 [视频-长播] <|video_begin|> + 09:22 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> + 09:39 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:13 [视频-长播] <|video_begin|> + 00:25 [视频-关注/长播] <|video_begin|> + 07:16 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> +【2026-02-04】 + 09:57 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:08 [视频-长播] <|video_begin|> + 10:10 [视频-长播] <|video_begin|> + 10:15 [广告-点击] <|ad_begin|> + 10:24 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 12:28 [视频-评论/点赞/长播] <|video_begin|> + 12:36 [视频-长播] <|video_begin|> + 12:39 [视频-长播] <|video_begin|> + 12:47 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:58 [广告-点击] <|ad_begin|> + 23:27 [广告-点击] <|ad_begin|> +【2026-02-05】 + --:-- [商品-点击] <|prod_begin|> + 05:15 [视频-长播] <|video_begin|> + 05:26 [广告-点击] <|ad_begin|> + 05:46 [视频-长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 05:49 [视频-长播] <|video_begin|> + 05:56 [视频-长播] <|video_begin|> + 05:56 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:17 [视频-长播] <|video_begin|> + 06:17 [视频-长播] <|video_begin|> + 06:17 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:22 [视频-长播] <|video_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:30 [广告-点击] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> + 08:29 [视频-点赞/长播] <|video_begin|> + 08:29 [视频-点赞/长播] <|video_begin|> + 08:41 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:44 [视频-点赞/长播] <|video_begin|> + 08:55 [视频-点赞/长播] <|video_begin|> + 08:55 [视频-点赞/长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:59 [视频-点赞/长播] <|video_begin|> + 08:59 [视频-点赞/长播] <|video_begin|> + 08:59 [视频-点赞/长播] <|video_begin|> + 08:59 [视频-长播] <|video_begin|> + 08:59 [视频-长播] <|video_begin|> + 09:03 [视频-点赞/长播] <|video_begin|> + 09:03 [视频-点赞/长播] <|video_begin|> + 09:03 [视频-点赞/长播] <|video_begin|> + 09:03 [视频-点赞/长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:08 [视频-点赞/长播] <|video_begin|> + 09:08 [视频-点赞/长播] <|video_begin|> + 09:08 [视频-长播] <|video_begin|> + 09:14 [视频-点赞/长播] <|video_begin|> + 09:14 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-点赞/长播] <|video_begin|> + 09:17 [视频-长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 09:27 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:39 [视频-点赞/长播] <|video_begin|> + 10:50 [视频-点赞/长播] <|video_begin|> + 10:50 [视频-点赞/长播] <|video_begin|> + 10:50 [视频-点赞/长播] <|video_begin|> + 10:50 [视频-点赞/长播] <|video_begin|> + 11:12 [视频-点赞/长播] <|video_begin|> + 11:12 [视频-点赞/长播] <|video_begin|> + 11:12 [视频-点赞/长播] <|video_begin|> + 11:12 [视频-点赞/长播] <|video_begin|> + 11:30 [视频-点赞/长播] <|video_begin|> + 11:30 [视频-点赞/长播] <|video_begin|> + 11:30 [视频-长播] <|video_begin|> + 11:30 [广告-点击] <|ad_begin|> + 11:45 [视频-点赞/长播] <|video_begin|> + 11:45 [视频-点赞/长播] <|video_begin|> + 11:45 [视频-点赞/长播] <|video_begin|> + 11:55 [视频-点赞/长播] <|video_begin|> + 12:02 [视频-点赞/长播] <|video_begin|> + 12:02 [视频-点赞/长播] <|video_begin|> + 12:02 [视频-点赞/长播] <|video_begin|> + 12:02 [视频-点赞/长播] <|video_begin|> + 12:09 [视频-点赞/长播] <|video_begin|> + 12:09 [视频-点赞/长播] <|video_begin|> + 12:09 [视频-点赞/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的身份，基于以上交互历史，针对给定主题筛选并提取出全部相关的历史交互行为。 + +主题：从被动触达到深度卷入的网赚游戏认知陷阱 + +输出格式要求：请仅以包含 SID 的 JSON 数组形式返回结果，切勿输出任何额外的解释说明或无关字符。/no_think<|im_end|> +<|im_start|>assistant + + +["<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-03-15】 + --:-- [商品-购买] <|prod_begin|> +【2023-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-12】 + --:-- [直播-打赏] <|living_begin|> +【2025-07-27】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-02】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-05】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-08-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-08-18】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-08-21】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-02】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-04】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-08】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-15】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> +【2025-11-11】 + --:-- [直播-关注] <|living_begin|> +【2025-11-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-04】 + --:-- [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|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-26】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-28】 + 00:37 [广告-深度转化] <|ad_begin|> + 03:51 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + --:-- [直播-关注] <|living_begin|> + 13:32 [广告-深度转化] <|ad_begin|> + 14:35 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 00:17 [广告-深度转化] <|ad_begin|> + 16:08 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 12:18 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + --:-- [商品-点击] <|prod_begin|> + 04:05 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 08:06 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + 14:26 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 01:56 [广告-深度转化] <|ad_begin|> + 08:05 [广告-深度转化] <|ad_begin|> + 08:21 [广告-深度转化] <|ad_begin|> + 09:02 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-09】 + 00:19 [广告-深度转化] <|ad_begin|> + 01:07 [广告-深度转化] <|ad_begin|> + 10:20 [广告-深度转化] <|ad_begin|> + 10:48 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [商品-点击] <|prod_begin|> + 03:08 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 03:56 [广告-深度转化] <|ad_begin|> + 04:47 [广告-深度转化] <|ad_begin|> + 07:31 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 09:41 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 11:19 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 09:59 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + 12:42 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:41 [广告-深度转化] <|ad_begin|> + 15:42 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 00:35 [广告-深度转化] <|ad_begin|> + 09:59 [广告-点击] <|ad_begin|> + 11:02 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:32 [广告-点击] <|ad_begin|> + 14:16 [广告-点击] <|ad_begin|> + 14:28 [广告-点击] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 22:47 [广告-深度转化] <|ad_begin|> + 22:47 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 02:59 [广告-深度转化] <|ad_begin|> + 02:59 [广告-点击] <|ad_begin|> + 10:03 [广告-点击] <|ad_begin|> + 12:23 [广告-点击] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> + 14:38 [广告-深度转化] <|ad_begin|> + 14:38 [广告-点击] <|ad_begin|> + 22:45 [广告-深度转化] <|ad_begin|> + 22:45 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:05 [广告-深度转化] <|ad_begin|> + 04:05 [广告-点击] <|ad_begin|> + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 08:39 [广告-点击] <|ad_begin|> + 09:24 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 15:25 [广告-点击] <|ad_begin|> + 15:35 [广告-深度转化] <|ad_begin|> + 15:35 [广告-点击] <|ad_begin|> + 17:14 [广告-深度转化] <|ad_begin|> + 17:14 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-点击] <|prod_begin|> + 09:36 [广告-深度转化] <|ad_begin|> + 09:36 [广告-点击] <|ad_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 09:45 [广告-点击] <|ad_begin|> + 23:28 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + 00:33 [广告-深度转化] <|ad_begin|> + 00:33 [广告-点击] <|ad_begin|> + 11:42 [广告-深度转化] <|ad_begin|> + 11:42 [广告-点击] <|ad_begin|> + 11:49 [广告-深度转化] <|ad_begin|> + 11:49 [广告-点击] <|ad_begin|> + 12:48 [广告-点击] <|ad_begin|> + 23:51 [视频-长播] <|video_begin|> +【2026-01-22】 + 00:02 [视频-长播] <|video_begin|> + 00:02 [视频-长播] <|video_begin|> + 00:02 [视频-长播] <|video_begin|> + 00:24 [广告-深度转化] <|ad_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:08 [广告-深度转化] <|ad_begin|> + 08:08 [广告-点击] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 08:46 [广告-点击] <|ad_begin|> + 08:54 [广告-深度转化] <|ad_begin|> + 08:54 [广告-点击] <|ad_begin|> + 10:38 [广告-深度转化] <|ad_begin|> + 10:38 [广告-点击] <|ad_begin|> + 10:45 [广告-深度转化] <|ad_begin|> + 10:45 [广告-点击] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> + 23:46 [广告-深度转化] <|ad_begin|> + 23:46 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 01:37 [广告-深度转化] <|ad_begin|> + 01:37 [广告-点击] <|ad_begin|> + 02:25 [视频-长播] <|video_begin|> + 06:38 [视频-长播] <|video_begin|> +【2026-01-24】 + --:-- [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/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 04:07 [视频-长播] <|video_begin|> + 04:07 [视频-长播] <|video_begin|> + 04:07 [视频-长播] <|video_begin|> + 09:50 [广告-点击] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> +【2026-01-25】 + 01:04 [视频-长播] <|video_begin|> + 03:50 [视频-长播] <|video_begin|> + 13:28 [广告-深度转化] <|ad_begin|> + 13:28 [广告-点击] <|ad_begin|> + 15:53 [广告-深度转化] <|ad_begin|> + 15:53 [广告-点击] <|ad_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + 03:19 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 05:23 [广告-点击] <|ad_begin|> + 09:56 [视频-长播] <|video_begin|> + 10:32 [视频-长播] <|video_begin|> + 11:02 [广告-点击] <|ad_begin|> + 12:31 [广告-深度转化] <|ad_begin|> + 12:31 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + 01:05 [广告-点击] <|ad_begin|> + 01:14 [广告-深度转化] <|ad_begin|> + 01:14 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 12:04 [广告-深度转化] <|ad_begin|> + 12:04 [广告-点击] <|ad_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + 14:41 [广告-深度转化] <|ad_begin|> + 14:41 [广告-点击] <|ad_begin|> + 14:48 [广告-深度转化] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 14:58 [视频-长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 00:59 [视频-长播] <|video_begin|> + 05:12 [视频-长播] <|video_begin|> + 05:27 [视频-长播] <|video_begin|> + 05:42 [广告-点击] <|ad_begin|> + 06:15 [视频-长播] <|video_begin|> + 06:15 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 11:23 [广告-点击] <|ad_begin|> + 11:29 [视频-关注/收藏/点赞/长播] <|video_begin|> +【2026-02-01】 + --:-- [直播-首次打赏] <|living_begin|> + 14:00 [广告-深度转化] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 14:01 [广告-点击] <|ad_begin|> + 14:15 [广告-深度转化] <|ad_begin|> + 14:15 [广告-点击] <|ad_begin|> + 14:18 [广告-点击] <|ad_begin|> + 15:33 [视频-长播] <|video_begin|> + 15:48 [视频-长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 00:40 [视频-关注/收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 21:47 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 01:16 [广告-深度转化] <|ad_begin|> + 01:16 [广告-点击] <|ad_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:29 [视频-长播] <|video_begin|> + 09:30 [广告-点击] <|ad_begin|> + 09:50 [广告-深度转化] <|ad_begin|> + 09:50 [广告-点击] <|ad_begin|> + 17:59 [视频-长播] <|video_begin|> + 17:59 [视频-长播] <|video_begin|> + 17:59 [视频-长播] <|video_begin|> + 17:59 [视频-收藏/点赞/长播] <|video_begin|> + 18:25 [广告-深度转化] <|ad_begin|> + 18:25 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 03:18 [视频-长播] <|video_begin|> + 04:02 [广告-点击] <|ad_begin|> + 04:17 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> + 04:57 [广告-点击] <|ad_begin|> + 06:43 [广告-点击] <|ad_begin|> + 06:44 [视频-长播] <|video_begin|> + 08:41 [广告-点击] <|ad_begin|> + 08:42 [视频-长播] <|video_begin|> + 08:44 [视频-长播] <|video_begin|> + 10:36 [视频-长播] <|video_begin|> + 10:59 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:01 [广告-点击] <|ad_begin|> + 13:36 [视频-关注/收藏/点赞/长播] <|video_begin|> + 13:51 [广告-深度转化] <|ad_begin|> + 13:51 [广告-点击] <|ad_begin|> + 14:20 [广告-点击] <|ad_begin|> +角色任务：你作为极端严苛的用户行为数据挖掘专家和数据格式化专家，需要根据以上用户交互历史，围绕给定主题抽取存在行为递进逻辑与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心判定逻辑（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。 +请根据以下主题提取行为逻辑链： +主题：电动车电瓶维护需求的逐步升级与方案探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "电动车电瓶维护需求的逐步升级与方案探索", + "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":"电动车电瓶维护需求的逐步升级与方案探索","events":[{"date":"2025-08-15","action":"[商品-点击] <|prod_begin|>","logic":"问题触发：初步关注点在于充电安全，通过点击“自动断电”、“防过载”保护器，解决基础的充电安全隐患。"},{"date":"2026-01-08","action":"[商品-点击] <|prod_begin|>","logic":"认知深化：在解决了充电安全问题后，需求升级。用户开始探索“电瓶修复器”、“激活修复”等产品，意图从被动保护转向主动延长电瓶寿命，解决性能衰退问题。"},{"date":"2026-01-10","action":"[商品-点击] <|prod_begin|>","logic":"方案探索与升级：在“修复”概念的基础上，引入了“太阳能”、“养护”、“光伏”等新变量。这表明用户不仅想修复现有问题，还在寻求一种更优、更具科技感的长期维护方案，认知层次再次提升。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-04-27】 + --:-- [直播-关注] <|living_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [直播-打赏] <|living_begin|> +【2025-12-08】 + 16:08 [广告-点击] <|ad_begin|> +【2025-12-09】 + 08:29 [广告-点击] <|ad_begin|> +【2025-12-10】 + 10:25 [广告-点击] <|ad_begin|> + 22:17 [广告-点击] <|ad_begin|> +【2025-12-11】 + 02:15 [广告-点击] <|ad_begin|> + 11:04 [广告-点击] <|ad_begin|> + 17:14 [广告-点击] <|ad_begin|> +【2025-12-12】 + 02:17 [广告-点击] <|ad_begin|> + 05:39 [广告-点击] <|ad_begin|> +【2025-12-14】 + 19:39 [广告-点击] <|ad_begin|> +【2025-12-15】 + 01:23 [广告-点击] <|ad_begin|> + 02:24 [广告-点击] <|ad_begin|> + 15:22 [广告-点击] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> +【2025-12-16】 + 01:01 [广告-点击] <|ad_begin|> + 04:55 [广告-点击] <|ad_begin|> + 04:56 [广告-点击] <|ad_begin|> + 08:22 [广告-点击] <|ad_begin|> + 10:20 [广告-点击] <|ad_begin|> + 20:09 [广告-点击] <|ad_begin|> +【2025-12-17】 + 00:44 [广告-点击] <|ad_begin|> + 05:14 [广告-点击] <|ad_begin|> +【2025-12-19】 + 04:16 [广告-点击] <|ad_begin|> +【2025-12-23】 + 03:32 [广告-点击] <|ad_begin|> +【2025-12-28】 + 11:24 [广告-点击] <|ad_begin|> +【2025-12-29】 + 03:23 [广告-点击] <|ad_begin|> +【2026-01-05】 + 15:57 [广告-点击] <|ad_begin|> +【2026-01-06】 + 12:34 [广告-点击] <|ad_begin|> +【2026-01-07】 + 02:15 [广告-点击] <|ad_begin|> + 10:42 [广告-点击] <|ad_begin|> + 13:14 [广告-点击] <|ad_begin|> +【2026-01-08】 + 02:49 [广告-点击] <|ad_begin|> +【2026-01-13】 + 01:39 [广告-点击] <|ad_begin|> +【2026-01-14】 + 09:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + 08:27 [广告-点击] <|ad_begin|> + 10:37 [广告-点击] <|ad_begin|> + 17:09 [广告-点击] <|ad_begin|> +【2026-01-18】 + 00:48 [广告-点击] <|ad_begin|> + 01:10 [广告-点击] <|ad_begin|> + 01:55 [广告-点击] <|ad_begin|> + 02:47 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-22】 + 02:01 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [直播-关注] <|living_begin|> + 03:00 [广告-点击] <|ad_begin|> +【2026-01-25】 + 01:35 [广告-点击] <|ad_begin|> +【2026-01-26】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [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|> +【2026-01-28】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + 06:20 [广告-点击] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> + 12:36 [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|> +【2026-01-30】 + 11:32 [广告-点击] <|ad_begin|> + 13:46 [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|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-31】 + 04:20 [广告-点击] <|ad_begin|> + 13:44 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:46 [广告-点击] <|ad_begin|> +【2026-02-01】 + 17:17 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-02】 + 09:31 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-03】 + 03:07 [广告-点击] <|ad_begin|> + 16:52 [广告-点击] <|ad_begin|> + 17:10 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-02-04】 + 04:06 [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|> +【2026-02-05】 + 03:40 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家和数据格式化专家，请基于以上用户交互历史，围绕指定主题提取具备行为递进关系与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +2.兴趣因果递进：交互 B 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +请针对以下主题提取行为逻辑链： +主题：从泛化理财兴趣到聚焦特定产品形态的认知收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化理财兴趣到聚焦特定产品形态的认知收敛", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这项任务。任务要求我基于用户的交互历史，挖掘出一条从泛化理财兴趣到聚焦特定产品形态的认知收敛行为链。我需要以第一人称的视角，详细阐述我的推理过程，并最终总结出符合要求的行为链条。 + +我首先通览了用户的全部行为时间线，发现用户的行为可以大致分为几个阶段。在2025年，用户的行为主要集中在关注各类直播上，涵盖了美妆护肤、美食、穿搭等多个领域，展现出对生活品质和个人形象的关注。然而，从2025年12月8日开始，用户的行为模式出现了显著的变化。 + +我注意到，在12月8日，用户首次点击了一则关于网商银行理财顾问的广告 <|ad_begin|>。这则广告内容比较宽泛，主要是传递理财产品的概念和优势，我判断这可能是用户对理财产生初步兴趣的起点。在此之后，用户的广告点击行为开始高度集中。在整个12月，我观察到用户密集点击了大量网商银行相关的理财广告，这些广告反复强调了安全性、收益率和投资风险等概念。例如，用户多次点击了由女性在家庭或职场环境中讲解理财的广告 <|ad_begin|>、<|ad_begin|>，这表明用户可能对与自身生活场景相关的财务规划内容更有代入感。这个阶段，用户的兴趣还停留在“理财”这个大概念上，处于信息搜集和建立基础认知的阶段。 + +进入2026年1月，用户的理财兴趣开始出现细化的迹象。在1月7日，我发现用户点击了一则明确提到具体产品系列的广告 <|ad_begin|>。这则广告不仅介绍了网商银行的理财产品，还特别点出了“周利宝”系列，并给出了“7天期限”、“年化收益率约2.5%”等具体的产品参数。我认为这是一个关键的转折点，标志着用户的认知从宽泛的理财概念，开始聚焦到具有特定形态和明确收益预期的产品上。这说明用户已经不满足于笼统的理财宣传，开始主动了解和比较具体的产品细节了。 + +随后，用户的行为进一步印证了我的推测。在1月下旬，用户进行了密集的购物行为，购买了大量食品、酒水、服饰、金饰和家居用品，例如购买了白酒礼盒 <|prod_begin|> 和足金手链 <|prod_begin|>，显示出较强的消费能力和对生活品质的追求。这种消费行为可能也反过来刺激了用户对闲置资金管理的思考。 + +到了2月5日，用户的兴趣收敛过程变得更加清晰。在这一天，用户再次点击了一则关于“周利宝”的广告 <|ad_begin|>。与之前的广告不同，这则广告的内容更加场景化，直接面向“年轻职场人士”，强调了“低门槛”、“低风险”和“资金灵活运用”等特性。我推断，用户在看到这则广告时，很可能产生了强烈的场景代入感，将自身情况与广告所描绘的用户画像进行了匹配。这表明用户的认知已经从单纯了解产品参数，深化到了评估该产品是否符合自身财务状况和生活需求的阶段，完成了从产品了解到自我代入的认知闭环。同日，用户还点击了大量商品，包括奢侈品 、家电 <|prod_begin|> 和保险产品 <|prod_begin|>，这进一步说明用户正处于一个综合性的财务规划和消费决策阶段，而对“周利宝”这类产品的关注，正是其理财规划中一个具体的落点。 + +综合以上分析，我认为用户的理财兴趣演化路径非常清晰，展现了一个从被动接收泛化概念，到主动关注具体产品，再到结合自身场景进行决策评估的完整认知过程。 + +因此，我提取的行为逻辑链如下： +首先，用户在2025年12月8日点击了关于网商银行理财顾问的广告 <|ad_begin|>，这代表了用户对理财概念的初步接触。随后，在2026年1月7日，用户点击了明确介绍“周利宝”系列并包含具体期限和收益率的广告 <|ad_begin|>，这标志着其兴趣开始向具体产品形态聚焦。最后，在2026年2月5日，用户点击了面向年轻职场人士、强调低门槛和资金灵活的“周利宝”广告 <|ad_begin|>，完成了从产品了解到与自身场景结合的认知深化。 +{"logic_chain": {"name": "从泛化理财兴趣到聚焦特定产品形态的认知收敛", "events": [{"date": "2025-12-08", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收泛化的银行理财概念广告，建立初步认知。"}, {"date": "2026-01-07", "action": "[广告-点击] <|ad_begin|>", "logic": "认知聚焦：从宽泛的理财概念转向关注具体产品形态（“周利宝”），并引入了新的决策变量（7天期限、2.5%年化收益率），体现了认知增量。"}, {"date": "2026-02-05", "action": "[广告-点击] <|ad_begin|>", "logic": "决策强化与场景代入：进一步关注“周利宝”产品，且信息维度从产品参数深化到目标人群（年轻职场人士）和应用场景（低门槛、资金灵活），完成了从产品了解到自我代入的认知闭环。"}]}}<|im_end|> +<|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, -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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"认知深化与路径修正：从被动点击广告转向主动观看长视频。用户不再满足于一键清理的模糊概念，而是开始探索更具体的应用层级操作（如微信权限管理），这代表了从“系统性能”的泛焦虑，收敛到对“特定App功能”的深度探究，是典型的认知增量和行为模式升级。"}]}}<|im_end|> + + +["<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|video_begin|>"]<|im_end|> +{"logic_chain":{"name":"电动车电瓶维护需求的逐步升级与方案探索","events":[{"date":"2025-08-15","action":"[商品-点击] <|prod_begin|>","logic":"问题触发：初步关注点在于充电安全，通过点击“自动断电”、“防过载”保护器，解决基础的充电安全隐患。"},{"date":"2026-01-08","action":"[商品-点击] <|prod_begin|>","logic":"认知深化：在解决了充电安全问题后，需求升级。用户开始探索“电瓶修复器”、“激活修复”等产品，意图从被动保护转向主动延长电瓶寿命，解决性能衰退问题。"},{"date":"2026-01-10","action":"[商品-点击] <|prod_begin|>","logic":"方案探索与升级：在“修复”概念的基础上，引入了“太阳能”、“养护”、“光伏”等新变量。这表明用户不仅想修复现有问题，还在寻求一种更优、更具科技感的长期维护方案，认知层次再次提升。"}]}}<|im_end|> + +好的，我来分析一下这项任务。任务要求我基于用户的交互历史，挖掘出一条从泛化理财兴趣到聚焦特定产品形态的认知收敛行为链。我需要以第一人称的视角，详细阐述我的推理过程，并最终总结出符合要求的行为链条。 + +我首先通览了用户的全部行为时间线，发现用户的行为可以大致分为几个阶段。在2025年，用户的行为主要集中在关注各类直播上，涵盖了美妆护肤、美食、穿搭等多个领域，展现出对生活品质和个人形象的关注。然而，从2025年12月8日开始，用户的行为模式出现了显著的变化。 + +我注意到，在12月8日，用户首次点击了一则关于网商银行理财顾问的广告 <|ad_begin|>。这则广告内容比较宽泛，主要是传递理财产品的概念和优势，我判断这可能是用户对理财产生初步兴趣的起点。在此之后，用户的广告点击行为开始高度集中。在整个12月，我观察到用户密集点击了大量网商银行相关的理财广告，这些广告反复强调了安全性、收益率和投资风险等概念。例如，用户多次点击了由女性在家庭或职场环境中讲解理财的广告 <|ad_begin|>、<|ad_begin|>，这表明用户可能对与自身生活场景相关的财务规划内容更有代入感。这个阶段，用户的兴趣还停留在“理财”这个大概念上，处于信息搜集和建立基础认知的阶段。 + +进入2026年1月，用户的理财兴趣开始出现细化的迹象。在1月7日，我发现用户点击了一则明确提到具体产品系列的广告 <|ad_begin|>。这则广告不仅介绍了网商银行的理财产品，还特别点出了“周利宝”系列，并给出了“7天期限”、“年化收益率约2.5%”等具体的产品参数。我认为这是一个关键的转折点，标志着用户的认知从宽泛的理财概念，开始聚焦到具有特定形态和明确收益预期的产品上。这说明用户已经不满足于笼统的理财宣传，开始主动了解和比较具体的产品细节了。 + +随后，用户的行为进一步印证了我的推测。在1月下旬，用户进行了密集的购物行为，购买了大量食品、酒水、服饰、金饰和家居用品，例如购买了白酒礼盒 <|prod_begin|> 和足金手链 <|prod_begin|>，显示出较强的消费能力和对生活品质的追求。这种消费行为可能也反过来刺激了用户对闲置资金管理的思考。 + +到了2月5日，用户的兴趣收敛过程变得更加清晰。在这一天，用户再次点击了一则关于“周利宝”的广告 <|ad_begin|>。与之前的广告不同，这则广告的内容更加场景化，直接面向“年轻职场人士”，强调了“低门槛”、“低风险”和“资金灵活运用”等特性。我推断，用户在看到这则广告时，很可能产生了强烈的场景代入感，将自身情况与广告所描绘的用户画像进行了匹配。这表明用户的认知已经从单纯了解产品参数，深化到了评估该产品是否符合自身财务状况和生活需求的阶段，完成了从产品了解到自我代入的认知闭环。同日，用户还点击了大量商品，包括奢侈品 、家电 <|prod_begin|> 和保险产品 <|prod_begin|>，这进一步说明用户正处于一个综合性的财务规划和消费决策阶段，而对“周利宝”这类产品的关注，正是其理财规划中一个具体的落点。 + +综合以上分析，我认为用户的理财兴趣演化路径非常清晰，展现了一个从被动接收泛化概念，到主动关注具体产品，再到结合自身场景进行决策评估的完整认知过程。 + +因此，我提取的行为逻辑链如下： +首先，用户在2025年12月8日点击了关于网商银行理财顾问的广告 <|ad_begin|>，这代表了用户对理财概念的初步接触。随后，在2026年1月7日，用户点击了明确介绍“周利宝”系列并包含具体期限和收益率的广告 <|ad_begin|>，这标志着其兴趣开始向具体产品形态聚焦。最后，在2026年2月5日，用户点击了面向年轻职场人士、强调低门槛和资金灵活的“周利宝”广告 <|ad_begin|>，完成了从产品了解到与自身场景结合的认知深化。 +{"logic_chain": {"name": "从泛化理财兴趣到聚焦特定产品形态的认知收敛", "events": [{"date": "2025-12-08", "action": "[广告-点击] <|ad_begin|>", "logic": "初始触发：被动接收泛化的银行理财概念广告，建立初步认知。"}, {"date": "2026-01-07", "action": "[广告-点击] <|ad_begin|>", "logic": "认知聚焦：从宽泛的理财概念转向关注具体产品形态（“周利宝”），并引入了新的决策变量（7天期限、2.5%年化收益率），体现了认知增量。"}, {"date": "2026-02-05", "action": "[广告-点击] <|ad_begin|>", "logic": "决策强化与场景代入：进一步关注“周利宝”产品，且信息维度从产品参数深化到目标人群（年轻职场人士）和应用场景（低门槛、资金灵活），完成了从产品了解到自我代入的认知闭环。"}]}}<|im_end|> +[INFO|configuration_utils.py:771] 2026-07-10 01:16:57,558 >> 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] 2026-07-10 01:16:57,558 >> 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|2026-07-10 01:16:57] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-10 01:16:57] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-10 01:16:58,466 >> 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] 2026-07-10 01:16:58,466 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-10 01:16:58,518 >> 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] 2026-07-10 01:16:59,483 >> 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] 2026-07-10 01:16:59,851 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-10 01:16:59] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-10 01:16:59] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-10 01:16:59] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-10 01:16:59] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-10 01:16:59] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-10 01:16:59,874 >> 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] 2026-07-10 01:16:59,990 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-10 01:16:59,990 >> Num examples = 2,573 +[INFO|trainer.py:1471] 2026-07-10 01:16:59,990 >> Num Epochs = 5 +[INFO|trainer.py:1472] 2026-07-10 01:16:59,990 >> Num update steps per epoch = 644 +[INFO|trainer.py:1473] 2026-07-10 01:16:59,990 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-10 01:16:59,990 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-10 01:16:59,990 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-10 01:16:59,990 >> Total optimization steps = 3,220 +[INFO|trainer.py:1479] 2026-07-10 01:16:59,990 >> Number of trainable parameters = 801,433,600 + 0%| | 0/3220 [00:00> `use_return_dict` is deprecated! 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>> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 3220/3220 [7:16:14<00:00, 6.50s/it] 100%|██████████| 3220/3220 [7:16:14<00:00, 8.13s/it] +[INFO|trainer.py:3815] 2026-07-10 08:33:14,198 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5 +[INFO|configuration_utils.py:533] 2026-07-10 08:33:14,199 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/config.json +[INFO|configuration_utils.py:816] 2026-07-10 08:33:14,199 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/generation_config.json + +{'loss': '1.789', 'grad_norm': '2.422', 'learning_rate': '2.169e-08', 'epoch': '4.737'} +{'loss': '1.767', 'grad_norm': '2.984', 'learning_rate': '2.045e-08', 'epoch': '4.745'} +{'loss': '1.776', 'grad_norm': '2.703', 'learning_rate': '1.923e-08', 'epoch': '4.752'} +{'loss': '1.781', 'grad_norm': '2.656', 'learning_rate': '1.806e-08', 'epoch': '4.76'} +{'loss': '1.793', 'grad_norm': '2.422', 'learning_rate': '1.692e-08', 'epoch': '4.768'} +{'loss': '1.762', 'grad_norm': '3.656', 'learning_rate': '1.582e-08', 'epoch': '4.776'} +{'loss': '1.765', 'grad_norm': '3.688', 'learning_rate': '1.476e-08', 'epoch': '4.784'} +{'loss': '1.77', 'grad_norm': '2.859', 'learning_rate': '1.373e-08', 'epoch': '4.791'} +{'loss': '1.762', 'grad_norm': '3.078', 'learning_rate': '1.274e-08', 'epoch': '4.799'} +{'loss': '1.789', 'grad_norm': '2.812', 'learning_rate': '1.179e-08', 'epoch': '4.807'} +{'loss': '1.751', 'grad_norm': '2.844', 'learning_rate': '1.087e-08', 'epoch': '4.815'} +{'loss': '1.73', 'grad_norm': '2.797', 'learning_rate': '9.995e-09', 'epoch': '4.822'} +{'loss': '1.768', 'grad_norm': '3.391', 'learning_rate': '9.153e-09', 'epoch': '4.83'} +{'loss': '1.744', 'grad_norm': '3.25', 'learning_rate': '8.348e-09', 'epoch': '4.838'} +{'loss': '1.757', 'grad_norm': '2.656', 'learning_rate': '7.579e-09', 'epoch': '4.846'} +{'loss': '1.782', 'grad_norm': '2.969', 'learning_rate': '6.848e-09', 'epoch': '4.853'} +{'loss': '1.755', 'grad_norm': '3.219', 'learning_rate': '6.154e-09', 'epoch': '4.861'} +{'loss': '1.747', 'grad_norm': '2.625', 'learning_rate': '5.497e-09', 'epoch': '4.869'} +{'loss': '1.767', 'grad_norm': '2.641', 'learning_rate': '4.876e-09', 'epoch': '4.877'} +{'loss': '1.745', 'grad_norm': '2.859', 'learning_rate': '4.293e-09', 'epoch': '4.885'} +{'loss': '1.778', 'grad_norm': '2.984', 'learning_rate': '3.747e-09', 'epoch': '4.892'} +{'loss': '1.765', 'grad_norm': '2.625', 'learning_rate': '3.238e-09', 'epoch': '4.9'} +{'loss': '1.767', 'grad_norm': '3.062', 'learning_rate': '2.766e-09', 'epoch': '4.908'} +{'loss': '1.759', 'grad_norm': '2.859', 'learning_rate': '2.331e-09', 'epoch': '4.916'} +{'loss': '1.781', 'grad_norm': '2.422', 'learning_rate': '1.934e-09', 'epoch': '4.923'} +{'loss': '1.799', 'grad_norm': '2.953', 'learning_rate': '1.573e-09', 'epoch': '4.931'} +{'loss': '1.76', 'grad_norm': '6.062', 'learning_rate': '1.25e-09', 'epoch': '4.939'} +{'loss': '1.738', 'grad_norm': '3.5', 'learning_rate': '9.637e-10', 'epoch': '4.947'} +{'loss': '1.709', 'grad_norm': '3.625', 'learning_rate': '7.146e-10', 'epoch': '4.955'} +{'loss': '1.774', 'grad_norm': '3.141', 'learning_rate': '5.027e-10', 'epoch': '4.962'} +{'loss': '1.752', 'grad_norm': '2.484', 'learning_rate': '3.279e-10', 'epoch': '4.97'} +{'loss': '1.759', 'grad_norm': '2.547', 'learning_rate': '1.904e-10', 'epoch': '4.978'} +{'loss': '1.757', 'grad_norm': '2.75', 'learning_rate': '8.998e-11', 'epoch': '4.986'} +{'loss': '1.736', 'grad_norm': '3.438', 'learning_rate': '2.677e-11', 'epoch': '4.993'} +{'loss': '1.77', 'grad_norm': '8.312', 'learning_rate': '7.436e-13', 'epoch': '5'} +{'train_runtime': '2.617e+04', 'train_samples_per_second': '0.492', 'train_steps_per_second': '0.123', 'train_loss': '1.818', 'epoch': '5'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-10 08:33:15,197 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-10 08:33:15,218 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/tokenizer_config.json +***** train metrics ***** + epoch = 5.0 + total_flos = 1442523792GF + train_loss = 1.818 + train_runtime = 7:16:14.20 + train_samples_per_second = 0.492 + train_steps_per_second = 0.123 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v23_scratch_clean_cot_guard_lr3e6_ep5/training_loss.png +[WARNING|2026-07-10 08:33:15] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-10 08:33:15] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-10 08:33:15,324 >> 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..dd1f335 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 5.0, + "total_flos": 1.5488981277124608e+18, + "train_loss": 1.8179572771795047, + "train_runtime": 26174.2071, + "train_samples_per_second": 0.492, + "train_steps_per_second": 0.123 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..1cd5210 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,645 @@ +{"current_steps": 5, "total_steps": 3220, "loss": 2.9617641448974608, "lr": 1.8461538461538464e-07, "epoch": 0.00777302759424796, "percentage": 0.16, "elapsed_time": "0:00:40", "remaining_time": "7:11:13"} +{"current_steps": 10, "total_steps": 3220, "loss": 2.940630340576172, "lr": 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