From f608bb332c1d13aac47fb54c4bc9e3c8d628dbfc Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 16 Aug 2026 12:51:17 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/v06_no_think_input_lr2e5 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + generation_config.json | 12 + model.safetensors | 3 + ...77306.ubuntu-System-Product-Name.1593888.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2328 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 81 + trainer_state.json | 603 +++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 42691 bytes 16 files changed, 3314 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul06_02-48-26_ubuntu-System-Product-Name/events.out.tfevents.1783277306.ubuntu-System-Product-Name.1593888.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz 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diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..bc9b24a --- /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: v06_no_think_input_lr2e5 + results: [] +--- + + + +# v06_no_think_input_lr2e5 + +This model is a fine-tuned version of [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the overnight_no_think_input dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 2e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607066 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 1 + +### Training results + + + +### Framework versions + +- Transformers 5.6.0 +- Pytorch 2.7.1+cu126 +- Datasets 4.0.0 +- Tokenizers 0.22.2 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..790f83d --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.913639661702574e+17, + "train_loss": 1.5678150677680969, + "train_runtime": 3206.5361, + "train_samples_per_second": 0.498, + "train_steps_per_second": 0.125 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..67162bb --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8562f0667a521d4408834d617460cd3e33ab1065196cd37b4391b3ce494a2dc3 +size 1602902832 diff --git a/runs/Jul06_02-48-26_ubuntu-System-Product-Name/events.out.tfevents.1783277306.ubuntu-System-Product-Name.1593888.0 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false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": false, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..f23ecb9 --- /dev/null +++ b/train.log @@ -0,0 +1,2328 @@ +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-06 02:47:54] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:771] 2026-07-06 02:47:54,897 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 02:47:54,899 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-06 02:47:58,681 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 02:47:58,682 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-06 02:47:59,059 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 02:47:59,060 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-06 02:48:00] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/20260706_overnight/data/no_think_input.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: 11673 examples [00:00, 109375.32 examples/s] Generating train split: 24719 examples [00:00, 117278.59 examples/s] Generating train split: 32480 examples [00:00, 119456.23 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/32480 [00:00user +【用户交互历史】： +【2025-01-10】 + --:-- [直播-关注] <|living_begin|> +【2025-01-27】 + --:-- [直播-关注] <|living_begin|> +【2025-02-12】 + --:-- [直播-关注] <|living_begin|> +【2025-02-21】 + --:-- [直播-关注] <|living_begin|> +【2025-02-28】 + --:-- [直播-关注] <|living_begin|> +【2025-03-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-30】 + --:-- [直播-关注] <|living_begin|> +【2025-04-04】 + --:-- [直播-关注] <|living_begin|> +【2025-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-21】 + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-07】 + --:-- [直播-关注] <|living_begin|> +【2025-05-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-19】 + --:-- [直播-关注] <|living_begin|> +【2025-06-30】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-22】 + --:-- [直播-关注] <|living_begin|> +【2025-07-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-30】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-02】 + --:-- [直播-关注] <|living_begin|> +【2025-10-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + 13:12 [广告-深度转化] <|ad_begin|> + 13:38 [广告-深度转化] <|ad_begin|> +【2025-12-09】 + --:-- [直播-打赏] <|living_begin|> + 11:55 [广告-深度转化] <|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|> + 23:04 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + 09:32 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:47 [广告-深度转化] <|ad_begin|> + 23:04 [广告-深度转化] <|ad_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> + 22:56 [广告-深度转化] <|ad_begin|> +【2025-12-14】 + 01:47 [广告-深度转化] <|ad_begin|> + 22:46 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> + 22:00 [广告-深度转化] <|ad_begin|> + 23:52 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 00:02 [广告-深度转化] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 08:24 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 22:56 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 23:01 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:17 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> + 09:19 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 01:11 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 07:42 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 08:53 [广告-深度转化] <|ad_begin|> + 09:45 [广告-深度转化] <|ad_begin|> + 12:12 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 08:53 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 08:27 [广告-深度转化] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 13:37 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 08:09 [广告-深度转化] <|ad_begin|> + 08:09 [广告-点击] <|ad_begin|> + 21:54 [广告-点击] <|ad_begin|> +【2026-01-04】 + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 12:43 [广告-深度转化] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> + 22:56 [广告-深度转化] <|ad_begin|> + 22:56 [广告-点击] <|ad_begin|> + 23:06 [广告-深度转化] <|ad_begin|> + 23:06 [广告-点击] <|ad_begin|> +【2026-01-05】 + 14:09 [广告-点击] <|ad_begin|> +【2026-01-06】 + 11:43 [广告-深度转化] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> +【2026-01-07】 + 10:50 [广告-深度转化] <|ad_begin|> + 10:50 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 23:39 [广告-点击] <|ad_begin|> +【2026-01-09】 + 05:59 [广告-点击] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> + 06:03 [广告-点击] <|ad_begin|> + 09:34 [广告-深度转化] <|ad_begin|> + 09:34 [广告-点击] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> + 13:47 [广告-点击] <|ad_begin|> +【2026-01-10】 + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> +【2026-01-11】 + 00:48 [广告-深度转化] <|ad_begin|> + 00:48 [广告-点击] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2026-01-12】 + 08:00 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:04 [广告-点击] <|ad_begin|> + 08:05 [广告-点击] <|ad_begin|> + 08:09 [广告-点击] <|ad_begin|> +【2026-01-13】 + 22:12 [广告-点击] <|ad_begin|> + 22:14 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> +【2026-01-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:11 [广告-深度转化] <|ad_begin|> + 13:11 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 05:54 [广告-深度转化] <|ad_begin|> + 05:54 [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|> + 23:33 [广告-深度转化] <|ad_begin|> + 23:33 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:12 [广告-深度转化] <|ad_begin|> + 13:12 [广告-点击] <|ad_begin|> + 22:21 [广告-点击] <|ad_begin|> +【2026-01-19】 + 10:02 [广告-深度转化] <|ad_begin|> + 10:02 [广告-点击] <|ad_begin|> + 11:40 [广告-深度转化] <|ad_begin|> + 11:40 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + 00:19 [广告-深度转化] <|ad_begin|> + 00:19 [广告-点击] <|ad_begin|> + 00:53 [广告-深度转化] <|ad_begin|> + 00:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-21】 + 03:14 [广告-深度转化] <|ad_begin|> + 03:14 [广告-点击] <|ad_begin|> + 04:27 [广告-深度转化] <|ad_begin|> + 04:27 [广告-点击] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 07:47 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-22】 + 05:51 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:38 [广告-点击] <|ad_begin|> +【2026-01-23】 + 01:36 [广告-点击] <|ad_begin|> + 01:42 [广告-深度转化] <|ad_begin|> + 01:42 [广告-点击] <|ad_begin|> + 04:17 [视频-点赞/长播] <|video_begin|> + 13:28 [视频-点赞/长播] <|video_begin|> + 14:54 [广告-深度转化] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> + 21:11 [广告-深度转化] <|ad_begin|> + 21:11 [广告-点击] <|ad_begin|> + 22:21 [视频-点赞/长播] <|video_begin|> + 22:21 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 09:53 [广告-深度转化] <|ad_begin|> + 09:53 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:48 [广告-深度转化] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> +【2026-01-25】 + 09:42 [广告-深度转化] <|ad_begin|> + 09:42 [广告-点击] <|ad_begin|> + 10:03 [视频-点赞/长播] <|video_begin|> + 10:32 [广告-深度转化] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-26】 + 00:28 [广告-深度转化] <|ad_begin|> + 00:28 [广告-点击] <|ad_begin|> + 05:46 [视频-点赞/长播] <|video_begin|> + 10:23 [广告-点击] <|ad_begin|> + 14:07 [广告-深度转化] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 14:10 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-点赞/长播] <|video_begin|> + 14:37 [视频-收藏/长播] <|video_begin|> + 14:49 [视频-点赞/长播] <|video_begin|> + 23:12 [视频-点赞/长播] <|video_begin|> + 23:12 [视频-点赞/长播] <|video_begin|> + 23:17 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 03:18 [视频-点赞/长播] <|video_begin|> + 03:18 [视频-点赞/长播] <|video_begin|> + 03:38 [广告-深度转化] <|ad_begin|> + 03:38 [广告-点击] <|ad_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> +【2026-01-28】 + 00:40 [广告-点击] <|ad_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:18 [视频-点赞/长播] <|video_begin|> + 09:26 [视频-点赞/长播] <|video_begin|> + 09:26 [视频-点赞/长播] <|video_begin|> + 09:44 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:00 [视频-点赞/长播] <|video_begin|> + 23:04 [广告-深度转化] <|ad_begin|> + 23:04 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:20 [广告-深度转化] <|ad_begin|> + 00:20 [广告-点击] <|ad_begin|> + 06:50 [广告-深度转化] <|ad_begin|> + 06:50 [广告-点击] <|ad_begin|> + 10:54 [视频-点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:18 [视频-长播] <|video_begin|> + 13:18 [视频-长播] <|video_begin|> + 13:18 [视频-点赞/长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-长播] <|video_begin|> + 13:22 [视频-点赞/长播] <|video_begin|> + 13:22 [视频-点赞/长播] <|video_begin|> + 13:57 [广告-深度转化] <|ad_begin|> + 13:57 [广告-点击] <|ad_begin|> + 23:07 [视频-点赞/长播] <|video_begin|> + 23:50 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:03 [视频-长播] <|video_begin|> + 00:03 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 22:03 [视频-长播] <|video_begin|> + 22:03 [视频-点赞/长播] <|video_begin|> + 22:04 [视频-长播] <|video_begin|> + 22:30 [视频-长播] <|video_begin|> +【2026-01-31】 + 06:56 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> + 10:34 [视频-点赞/长播] <|video_begin|> + 10:38 [广告-点击] <|ad_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:52 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> +【2026-02-01】 + 13:34 [视频-点赞/长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-收藏/长播] <|video_begin|> +【2026-02-02】 + 10:05 [视频-长播] <|video_begin|> + 10:53 [视频-长播] <|video_begin|> + 10:53 [视频-点赞/长播] <|video_begin|> + 10:53 [视频-点赞/长播] <|video_begin|> + 11:05 [视频-长播] <|video_begin|> + 11:08 [视频-长播] <|video_begin|> + 11:08 [视频-长播] <|video_begin|> + 11:13 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:41 [视频-长播] <|video_begin|> + 23:41 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:24 [广告-深度转化] <|ad_begin|> + 00:24 [广告-点击] <|ad_begin|> + 00:53 [广告-点击] <|ad_begin|> + 07:43 [视频-长播] <|video_begin|> + 08:34 [视频-点赞/长播] <|video_begin|> + 08:36 [视频-长播] <|video_begin|> + 08:36 [视频-点赞/长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:56 [广告-深度转化] <|ad_begin|> + 08:56 [广告-点击] <|ad_begin|> + 09:22 [视频-长播] <|video_begin|> + 09:22 [视频-点赞/长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:31 [视频-点赞/长播] <|video_begin|> + 09:31 [视频-点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:46 [视频-长播] <|video_begin|> + 12:53 [视频-长播] <|video_begin|> + 12:53 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 12:57 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 22:13 [视频-点赞/长播] <|video_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> + 22:56 [广告-深度转化] <|ad_begin|> + 22:56 [广告-点击] <|ad_begin|> +【2026-02-04】 + 14:38 [视频-长播] <|video_begin|> + 14:38 [视频-长播] <|video_begin|> + 22:55 [视频-长播] <|video_begin|> + 22:56 [视频-点赞/长播] <|video_begin|> + 23:30 [广告-深度转化] <|ad_begin|> + 23:30 [广告-点击] <|ad_begin|> +【2026-02-05】 + 00:21 [视频-点赞/长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-长播] <|video_begin|> + 00:32 [视频-点赞/长播] <|video_begin|> + 00:37 [视频-点赞/长播] <|video_begin|> + 00:44 [视频-长播] <|video_begin|> + 00:44 [视频-点赞/长播] <|video_begin|> + 00:47 [视频-点赞/长播] <|video_begin|> + 00:50 [视频-长播] <|video_begin|> + 00:53 [视频-长播] <|video_begin|> + 00:55 [视频-长播] <|video_begin|> + 00:56 [视频-点赞/长播] <|video_begin|> + 13:59 [视频-点赞/长播] <|video_begin|> +角色任务：你需要扮演极端严苛的用户行为数据挖掘专家以及数据格式化专家，基于以上用户交互历史，针对给定主题识别并提取具有递进逻辑和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）： +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 必须与交互 A 构成因果递进关系，即 B 来源于 A 带来的副作用、新需求，或 A 是 B 的触发诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请针对以下主题提取行为逻辑链： +主题：从被动信息吸收到主动利用AI工具的认知跃迁 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动信息吸收到主动利用AI工具的认知跃迁", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}<|im_end|> +<|im_start|>assistant + +好的，我将基于这份用户行为日志，以推荐分析师的视角，对用户的行为逻辑进行推理。 + +我的任务是分析用户从被动接收信息到主动探索并利用AI工具的认知转变过程。我会重点关注行为时间线，梳理出导致这一转变的关键节点和内在逻辑。 + +在分析初期，我注意到该用户是一个信息涉猎非常广泛的人，尤其偏爱知识型和资讯型内容。从2025年上半年开始，他就大量关注财经、时政、健康养生等领域的直播，例如解读A股趋势的直播 <|living_begin|>、分析国际局势的直播 <|living_begin|> 以及各类健康科普直播。这表明他有强烈的通过内容消费来获取信息、辅助决策的习惯。 + +随着时间推移，特别是在2026年1月下旬，我发现用户的行为出现了一个关键的转折点。在2026年1月26日，他密集地消费了大量关于股市、国际政治和经济动态的视频内容。在这些内容中，他不仅点赞，还特别收藏了一个分析股市牛熊转变与投资者心态的视频 <|video_begin|>。收藏这个行为对我来说是一个强烈的信号，它意味着用户不再满足于信息的简单浏览，而是希望对复杂的决策逻辑进行深度思考和复盘。我推测，面对海量且充满不确定性的财经信息，他可能感到了认知上的压力和决策上的痛点，从而萌生了寻找更高效信息处理方法或决策辅助工具的想法。 + +这个推测很快在接下来的行为中得到了印证。仅仅三天后，在2026年1月29日，他首次主动点击了一则关于免费人工智能课程的广告 <|ad_begin|>。这是一个非常重要的行为变化，标志着他开始将注意力从“信息本身”转移到了“处理信息的工具”上。他不再仅仅是被动地接收财经博主的观点，而是开始主动探索能够赋能自己的新技术。这说明他已经意识到，AI可能就是解决他当前信息处理瓶颈的潜在方案。 + +为了进一步确认他对“工具”和“效率”的追求，我继续观察后续行为。在2026年2月1日，他又收藏了一个关于荣耀手机七大实用功能的视频 <|video_begin|>，内容聚焦于如何通过手机自带的功能（如分屏、长截屏）来提升操作效率。这个行为虽然看似与AI没有直接关联，但其内在动机与探索AI工具是高度一致的，都指向了“利用工具提升个人效率”这一核心诉求。这表明他的认知已经发生了转变，从一个纯粹的内容消费者，开始向一个积极寻求并利用工具来优化个人工作流的“效率追求者”演变。 + +综合来看，我认为用户的行为逻辑链条清晰地展现了一个认知跃迁的过程。他最初通过大量观看财经、时政类视频积累信息，在信息过载中遇到了决策瓶颈，这具体体现在他收藏了关于投资策略与心理学研究的视频 <|video_begin|>上。这个痛点驱动他开始寻找解决方案，于是他主动点击了免费人工智能课程的广告 <|ad_begin|>，迈出了探索新工具的第一步。最后，他收藏手机高效操作技巧的视频 <|video_begin|>，进一步巩固了他对于利用工具提升效率的认知和兴趣。因此，我倾向于认为，该用户未来对各类能提升信息处理效率、辅助决策的AI工具或相关课程内容，会有较高的接受度和付费转化可能。 +{"logic_chain": {"name": "从被动信息吸收到主动利用AI工具的认知跃迁", "events": [{"date": "2026-01-26", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "信息积累与痛点形成：用户通过大量观看财经、科技、时政类视频，积累了海量信息，并收藏了关于“投资策略与心理学”的深度分析视频，表明其在信息处理和决策上可能遇到了瓶颈，产生了对更高效工具的需求。"}, {"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣转向与工具探索：用户开始主动点击“免费人工智能课程”广告，标志着其兴趣从被动接收信息转向主动学习和了解能提升信息处理效率的AI工具，行为模式发生显著变化。"}, {"date": "2026-02-01", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "认知深化与效率追求：用户收藏了关于“手机实用功能”以提升操作效率的视频。这一行为与探索AI工具的逻辑一致，共同指向其核心诉求——利用工具提升个人效率，进一步佐证了其从“信息消费”到“工具赋能”的认知转变。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2022-06-06】 + --:-- [商品-购买] <|prod_begin|> +【2022-09-11】 + --:-- [商品-购买] <|prod_begin|> +【2022-09-12】 + --:-- [商品-购买] <|prod_begin|> +【2022-09-13】 + --:-- [商品-购买] <|prod_begin|> +【2022-09-30】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2022-11-16】 + --:-- [商品-购买] <|prod_begin|> +【2022-12-18】 + --:-- [商品-购买] <|prod_begin|> +【2023-01-24】 + --:-- [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|> +【2023-01-25】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-11】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-15】 + --:-- [商品-购买] <|prod_begin|> +【2023-05-09】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-06】 + --:-- [商品-购买] <|prod_begin|> +【2023-11-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-01-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-01-31】 + --:-- [商品-点击] <|prod_begin|> +【2024-03-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-03-20】 + --:-- [商品-点击] <|prod_begin|> +【2024-04-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2024-05-07】 + --:-- [商品-点击] <|prod_begin|> +【2024-05-23】 + --:-- [商品-点击] <|prod_begin|> +【2024-05-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2024-06-18】 + --:-- [商品-点击] <|prod_begin|> +【2024-07-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-07-22】 + --:-- [商品-点击] <|prod_begin|> +【2024-07-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2024-08-01】 + --:-- [商品-点击] <|prod_begin|> +【2024-08-06】 + --:-- [商品-点击] <|prod_begin|> +【2024-08-08】 + --:-- [商品-点击] <|prod_begin|> +【2024-08-09】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-08-23】 + --:-- [商品-点击] <|prod_begin|> +【2024-09-07】 + --:-- [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-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-09-13】 + --:-- [商品-点击] <|prod_begin|> +【2024-09-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2024-12-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + 03:31 [广告-点击] <|ad_begin|> + 10:13 [广告-点击] <|ad_begin|> +【2025-12-12】 + 02:14 [广告-点击] <|ad_begin|> + 05:28 [广告-点击] <|ad_begin|> +【2025-12-16】 + 06:21 [广告-点击] <|ad_begin|> + 15:06 [广告-点击] <|ad_begin|> + 15:17 [广告-点击] <|ad_begin|> +【2025-12-17】 + 04:44 [广告-点击] <|ad_begin|> + 07:10 [广告-点击] <|ad_begin|> + 09:39 [广告-点击] <|ad_begin|> +【2025-12-20】 + 09:56 [广告-点击] <|ad_begin|> +【2025-12-23】 + 23:47 [广告-点击] <|ad_begin|> +【2025-12-24】 + 09:57 [广告-点击] <|ad_begin|> + 13:49 [广告-点击] <|ad_begin|> +【2025-12-26】 + 16:44 [广告-点击] <|ad_begin|> +【2025-12-28】 + 23:24 [广告-点击] <|ad_begin|> +【2025-12-29】 + 11:32 [广告-点击] <|ad_begin|> +【2025-12-30】 + 08:19 [广告-点击] <|ad_begin|> + 13:05 [广告-点击] <|ad_begin|> +【2026-01-02】 + 00:41 [广告-点击] <|ad_begin|> + 16:12 [广告-点击] <|ad_begin|> +【2026-01-03】 + 13:27 [广告-点击] <|ad_begin|> + 16:43 [广告-点击] <|ad_begin|> + 23:26 [广告-点击] <|ad_begin|> +【2026-01-04】 + 02:30 [广告-点击] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 23:50 [广告-点击] <|ad_begin|> +【2026-01-05】 + 01:39 [广告-点击] <|ad_begin|> +【2026-01-06】 + 08:45 [广告-点击] <|ad_begin|> +【2026-01-07】 + 09:56 [广告-点击] <|ad_begin|> +【2026-01-08】 + 06:18 [广告-点击] <|ad_begin|> +【2026-01-09】 + 00:45 [广告-点击] <|ad_begin|> +【2026-01-11】 + 07:41 [广告-点击] <|ad_begin|> +【2026-01-12】 + 10:28 [广告-点击] <|ad_begin|> +【2026-01-14】 + 02:17 [广告-点击] <|ad_begin|> +【2026-01-15】 + 10:20 [广告-点击] <|ad_begin|> +【2026-01-17】 + 11:25 [广告-点击] <|ad_begin|> + 13:49 [广告-点击] <|ad_begin|> + 15:09 [广告-点击] <|ad_begin|> +【2026-01-18】 + 02:50 [广告-点击] <|ad_begin|> + 04:54 [广告-点击] <|ad_begin|> + 23:04 [广告-点击] <|ad_begin|> +【2026-01-19】 + 02:19 [广告-点击] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 09:43 [广告-点击] <|ad_begin|> +【2026-01-21】 + 21:10 [广告-点击] <|ad_begin|> +【2026-01-23】 + 09:06 [视频-关注/点赞/长播] <|video_begin|> + 09:08 [广告-点击] <|ad_begin|> + 09:44 [视频-关注/长播] <|video_begin|> + 09:48 [广告-点击] <|ad_begin|> + 12:38 [广告-点击] <|ad_begin|> + 12:38 [视频-关注/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:52 [视频-关注/长播] <|video_begin|> + 01:18 [视频-收藏/长播] <|video_begin|> + 01:27 [视频-收藏/长播] <|video_begin|> + 08:02 [视频-关注/长播] <|video_begin|> + 10:18 [视频-关注/长播] <|video_begin|> + 10:27 [视频-关注/长播] <|video_begin|> + 12:21 [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-购买] <|prod_begin|> +【2026-01-25】 + --:-- [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|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 04:07 [视频-关注/长播] <|video_begin|> + 06:33 [视频-关注/长播] <|video_begin|> + 08:19 [广告-点击] <|ad_begin|> + 10:05 [视频-关注/长播] <|video_begin|> + 12:24 [视频-关注/收藏/点赞/长播] <|video_begin|> + 12:42 [广告-点击] <|ad_begin|> + 12:45 [视频-关注/长播] <|video_begin|> + 13:01 [视频-浏览] <|video_begin|> + 13:38 [视频-关注/点赞/长播] <|video_begin|> + 13:38 [视频-关注/收藏/点赞/长播] <|video_begin|> + 14:04 [视频-关注/收藏/点赞/长播] <|video_begin|> + 14:58 [视频-关注/收藏/点赞/长播] <|video_begin|> +【2026-01-26】 + 07:26 [广告-点击] <|ad_begin|> + 11:51 [广告-点击] <|ad_begin|> + 16:51 [广告-点击] <|ad_begin|> +【2026-01-27】 + 00:51 [广告-点击] <|ad_begin|> + 01:58 [广告-点击] <|ad_begin|> +【2026-01-28】 + 14:35 [广告-点击] <|ad_begin|> +【2026-01-29】 + 01:37 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/打赏] <|living_begin|> + 02:38 [广告-点击] <|ad_begin|> + 08:40 [视频-关注/点赞/长播] <|video_begin|> + 08:40 [视频-收藏/长播] <|video_begin|> + 08:49 [视频-关注/点赞/长播] <|video_begin|> + 09:16 [视频-关注/点赞/长播] <|video_begin|> + 09:16 [视频-关注/收藏/点赞/长播] <|video_begin|> + 09:34 [视频-关注/长播] <|video_begin|> + 10:03 [视频-关注/点赞/长播] <|video_begin|> + 10:20 [视频-关注/点赞/长播] <|video_begin|> + 10:20 [视频-关注/收藏/点赞/长播] <|video_begin|> + 15:17 [视频-关注/点赞/长播] <|video_begin|> + 15:17 [视频-关注/收藏/点赞/长播] <|video_begin|> + 16:36 [视频-关注/收藏/点赞/长播] <|video_begin|> + 16:43 [视频-关注/长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 03:05 [广告-点击] <|ad_begin|> + 11:27 [视频-收藏/长播] <|video_begin|> + 11:27 [视频-关注/收藏/点赞/长播] <|video_begin|> + 12:01 [广告-点击] <|ad_begin|> + 12:31 [视频-收藏/点赞/长播] <|video_begin|> + 13:17 [视频-关注/长播] <|video_begin|> + 13:41 [视频-收藏/点赞/长播] <|video_begin|> + 13:41 [视频-关注/长播] <|video_begin|> + 13:57 [视频-收藏/点赞/长播] <|video_begin|> + 14:05 [视频-收藏/点赞/长播] <|video_begin|> + 14:56 [视频-收藏/点赞/长播] <|video_begin|> + 14:56 [视频-收藏/点赞/长播] <|video_begin|> + 14:56 [视频-关注/长播] <|video_begin|> + 16:00 [视频-收藏/点赞/长播] <|video_begin|> + 16:06 [视频-收藏/点赞/长播] <|video_begin|> + 16:37 [视频-收藏/点赞/长播] <|video_begin|> + 16:45 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + 02:14 [视频-收藏/点赞/长播] <|video_begin|> + 02:47 [视频-收藏/点赞/长播] <|video_begin|> + 03:03 [视频-收藏/点赞/长播] <|video_begin|> + 03:09 [视频-收藏/点赞/长播] <|video_begin|> + 03:09 [视频-收藏/点赞/长播] <|video_begin|> + 03:12 [视频-收藏/点赞/长播] <|video_begin|> + 04:46 [视频-关注/点赞/长播] <|video_begin|> + 04:57 [视频-关注/长播] <|video_begin|> + 05:38 [视频-收藏/点赞/长播] <|video_begin|> + 05:42 [广告-点击] <|ad_begin|> + 05:53 [视频-收藏/点赞/长播] <|video_begin|> + 06:05 [广告-点击] <|ad_begin|> + 06:47 [视频-收藏/点赞/长播] <|video_begin|> + 06:49 [视频-收藏/点赞/长播] <|video_begin|> + 07:00 [视频-点赞/长播] <|video_begin|> + 07:22 [视频-收藏/点赞/长播] <|video_begin|> + 08:49 [视频-点赞/长播] <|video_begin|> + 08:49 [视频-收藏/点赞/长播] <|video_begin|> + 09:07 [视频-收藏/点赞/长播] <|video_begin|> + 09:13 [视频-浏览] <|video_begin|> + 09:19 [视频-收藏/点赞/长播] <|video_begin|> + 09:34 [视频-点赞/长播] <|video_begin|> + 09:40 [视频-点赞/长播] <|video_begin|> + 09:44 [视频-点赞/长播] <|video_begin|> + 09:50 [视频-点赞/长播] <|video_begin|> + 09:50 [视频-关注/收藏/点赞/长播] <|video_begin|> + 10:00 [视频-关注/收藏/点赞/长播] <|video_begin|> + 11:19 [视频-点赞/长播] <|video_begin|> + 11:32 [视频-点赞/长播] <|video_begin|> + 11:32 [视频-关注/收藏/点赞/长播] <|video_begin|> + 12:48 [视频-点赞/长播] <|video_begin|> + 12:48 [视频-收藏/点赞/长播] <|video_begin|> + 13:55 [视频-收藏/长播] <|video_begin|> + 14:43 [视频-收藏/点赞/长播] <|video_begin|> + 15:07 [视频-点赞/长播] <|video_begin|> + 15:35 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-关注/点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 01:52 [广告-点击] <|ad_begin|> + 02:43 [广告-点击] <|ad_begin|> + 06:02 [广告-点击] <|ad_begin|> + 10:58 [广告-点击] <|ad_begin|> +【2026-02-04】 + 13:36 [广告-点击] <|ad_begin|> +【2026-02-05】 + 03:19 [视频-关注/点赞/长播] <|video_begin|> + 03:25 [视频-收藏/点赞/长播] <|video_begin|> + 03:27 [视频-收藏/点赞/长播] <|video_begin|> + 03:29 [视频-点赞/长播] <|video_begin|> + 03:29 [视频-收藏/点赞/长播] <|video_begin|> + 03:51 [视频-收藏/点赞/长播] <|video_begin|> + 03:51 [视频-关注/收藏/点赞/长播] <|video_begin|> + 03:53 [视频-点赞/长播] <|video_begin|> + 03:53 [视频-收藏/点赞/长播] <|video_begin|> + 03:56 [视频-点赞/长播] <|video_begin|> + 04:08 [视频-关注/收藏/点赞/长播] <|video_begin|> + 04:12 [视频-点赞/长播] <|video_begin|> + 04:28 [视频-点赞/长播] <|video_begin|> + 04:32 [视频-点赞/长播] <|video_begin|> + 04:35 [视频-收藏/点赞/长播] <|video_begin|> + 04:35 [视频-收藏/点赞/长播] <|video_begin|> + 04:40 [视频-关注/长播] <|video_begin|> + 05:13 [视频-点赞/长播] <|video_begin|> + 05:16 [视频-点赞/长播] <|video_begin|> + 05:17 [视频-点赞/长播] <|video_begin|> + 05:17 [视频-收藏/点赞/长播] <|video_begin|> + 05:23 [视频-点赞/长播] <|video_begin|> + 05:36 [视频-点赞/长播] <|video_begin|> + 05:36 [视频-收藏/点赞/长播] <|video_begin|> + 05:43 [视频-关注/点赞/长播] <|video_begin|> + 06:51 [视频-点赞/长播] <|video_begin|> + 06:58 [视频-收藏/点赞/长播] <|video_begin|> + 07:03 [视频-关注/点赞/长播] <|video_begin|> + 07:05 [视频-点赞/长播] <|video_begin|> + 07:05 [视频-关注/点赞/长播] <|video_begin|> + 07:15 [视频-关注/长播] <|video_begin|> + 07:35 [视频-收藏/点赞/长播] <|video_begin|> + 07:41 [视频-点赞/长播] <|video_begin|> + 07:56 [视频-点赞/长播] <|video_begin|> + 08:38 [视频-点赞/长播] <|video_begin|> + 09:33 [视频-点赞/长播] <|video_begin|> + 10:00 [视频-收藏/点赞/长播] <|video_begin|> + 10:00 [视频-收藏/点赞/长播] <|video_begin|> + 10:06 [视频-点赞/长播] <|video_begin|> + 10:06 [视频-收藏/点赞/长播] <|video_begin|> + 10:14 [视频-收藏/点赞/长播] <|video_begin|> + 10:19 [视频-收藏/点赞/长播] <|video_begin|> + 10:19 [视频-收藏/点赞/长播] <|video_begin|> + 10:23 [视频-点赞/长播] <|video_begin|> + 10:24 [视频-点赞/长播] <|video_begin|> + 10:31 [视频-点赞/长播] <|video_begin|> + 10:38 [视频-收藏/点赞/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + 10:50 [视频-点赞/长播] <|video_begin|> + 11:23 [视频-点赞/长播] <|video_begin|> + 11:25 [视频-收藏/点赞/长播] <|video_begin|> + 11:26 [视频-点赞/长播] <|video_begin|> + 11:30 [视频-长播] <|video_begin|> + 11:30 [视频-长播] <|video_begin|> + 11:32 [视频-长播] <|video_begin|> + 11:32 [视频-长播] <|video_begin|> + 11:32 [视频-长播] <|video_begin|> + 11:35 [视频-长播] <|video_begin|> + 11:35 [视频-长播] <|video_begin|> + 11:45 [视频-长播] <|video_begin|> + 11:45 [视频-长播] <|video_begin|> + 11:46 [视频-长播] <|video_begin|> + 11:46 [视频-长播] <|video_begin|> + 11:49 [广告-点击] <|ad_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:52 [视频-长播] <|video_begin|> + 11:52 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 12:05 [视频-长播] <|video_begin|> + 12:05 [视频-长播] <|video_begin|> + 12:05 [视频-长播] <|video_begin|> + 12:05 [视频-关注/收藏/点赞/长播] <|video_begin|> + 12:08 [视频-长播] <|video_begin|> + 12:17 [视频-点赞/长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:17 [视频-长播] <|video_begin|> + 12:52 [视频-点赞/长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:52 [视频-长播] <|video_begin|> + 12:54 [视频-长播] <|video_begin|> + 12:54 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:06 [视频-点赞/长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 13:06 [视频-长播] <|video_begin|> + 13:46 [视频-长播] <|video_begin|> + 13:46 [视频-长播] <|video_begin|> + 13:46 [视频-收藏/点赞/长播] <|video_begin|> + 13:58 [视频-点赞/长播] <|video_begin|> + 13:58 [视频-点赞/长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 14:02 [视频-点赞/长播] <|video_begin|> + 14:02 [视频-点赞/长播] <|video_begin|> + 14:07 [视频-点赞/长播] <|video_begin|> + 14:07 [视频-长播] <|video_begin|> + 14:07 [视频-长播] <|video_begin|> + 14:10 [视频-长播] <|video_begin|> + 14:10 [视频-长播] <|video_begin|> + 14:15 [视频-点赞/长播] <|video_begin|> + 14:15 [视频-点赞/长播] <|video_begin|> + 14:15 [视频-关注/点赞/长播] <|video_begin|> + 14:26 [视频-长播] <|video_begin|> + 14:31 [视频-长播] <|video_begin|> + 14:31 [视频-收藏/点赞/长播] <|video_begin|> + 14:31 [视频-关注/长播] <|video_begin|> + 15:06 [视频-长播] <|video_begin|> + 15:06 [视频-长播] <|video_begin|> + 15:06 [视频-收藏/点赞/长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:11 [视频-关注/点赞/长播] <|video_begin|> + 15:19 [视频-点赞/长播] <|video_begin|> + 15:19 [视频-长播] <|video_begin|> + 15:19 [视频-关注/长播] <|video_begin|> + 15:22 [视频-点赞/长播] <|video_begin|> + 15:22 [视频-长播] <|video_begin|> + 15:22 [视频-收藏/点赞/长播] <|video_begin|> + 15:29 [视频-长播] <|video_begin|> + 15:29 [视频-长播] <|video_begin|> + 15:29 [视频-收藏/点赞/长播] <|video_begin|> + +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题，严格提取出所有相关的历史交互条目。 + +主题：从被动娱乐到主动探索的游戏深度演化 + +输出格式要求：请仅输出包含 SID 的 JSON 数组，严禁输出任何额外的解释、说明或 Markdown 标记。<|im_end|> +<|im_start|>assistant + + +["<|ad_begin|>", "<|ad_begin|>", "<|living_begin|>", "<|living_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-07-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-16】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-15】 + --:-- [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|> +【2025-11-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2025-11-20】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> +【2025-11-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-23】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-27】 + --:-- [直播-关注] <|living_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2025-12-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-03】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> +【2026-01-08】 + 02:15 [广告-深度转化] <|ad_begin|> + 03:33 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 06:17 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:41 [广告-深度转化] <|ad_begin|> + 03:17 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 03:28 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> + 12:28 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 07:27 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 01:24 [广告-深度转化] <|ad_begin|> + 11:04 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:02 [广告-深度转化] <|ad_begin|> + 01:05 [广告-深度转化] <|ad_begin|> + 01:15 [广告-深度转化] <|ad_begin|> + 12:27 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:48 [广告-深度转化] <|ad_begin|> + 04:10 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:14 [广告-深度转化] <|ad_begin|> + 08:17 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:55 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 04:30 [广告-深度转化] <|ad_begin|> + 04:43 [广告-深度转化] <|ad_begin|> + 06:28 [广告-深度转化] <|ad_begin|> + 06:37 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 08:12 [广告-深度转化] <|ad_begin|> + 12:20 [广告-深度转化] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + 02:20 [广告-深度转化] <|ad_begin|> + 15:08 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + 04:44 [广告-深度转化] <|ad_begin|> + 04:48 [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|> + 04:50 [广告-深度转化] <|ad_begin|> + 04:51 [广告-深度转化] <|ad_begin|> + 09:45 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + 02:37 [广告-深度转化] <|ad_begin|> + 02:49 [广告-深度转化] <|ad_begin|> + 03:03 [广告-深度转化] <|ad_begin|> + 03:17 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 01:08 [广告-深度转化] <|ad_begin|> + 01:14 [广告-深度转化] <|ad_begin|> + 01:29 [广告-深度转化] <|ad_begin|> + 01:32 [广告-深度转化] <|ad_begin|> + 01:35 [广告-深度转化] <|ad_begin|> + 01:37 [广告-深度转化] <|ad_begin|> + 01:37 [广告-点击] <|ad_begin|> + 01:37 [广告-点击] <|ad_begin|> + 01:40 [广告-深度转化] <|ad_begin|> + 01:40 [广告-点击] <|ad_begin|> + 01:41 [广告-深度转化] <|ad_begin|> + 01:41 [广告-点击] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 01:49 [广告-点击] <|ad_begin|> + 01:50 [广告-点击] <|ad_begin|> + 03:20 [广告-点击] <|ad_begin|> + 03:21 [广告-点击] <|ad_begin|> + 03:22 [广告-深度转化] <|ad_begin|> + 03:22 [广告-点击] <|ad_begin|> + 03:23 [广告-点击] <|ad_begin|> + 03:25 [广告-深度转化] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> + 03:27 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:54 [广告-点击] <|ad_begin|> + 11:57 [广告-深度转化] <|ad_begin|> + 11:57 [广告-点击] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> + 12:06 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 03:31 [广告-深度转化] <|ad_begin|> + 03:31 [广告-点击] <|ad_begin|> + 03:33 [广告-点击] <|ad_begin|> + 03:35 [广告-深度转化] <|ad_begin|> + 03:35 [广告-点击] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> + 13:16 [广告-点击] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:22 [广告-点击] <|ad_begin|> + 15:12 [广告-点击] <|ad_begin|> + 15:13 [广告-点击] <|ad_begin|> + 16:10 [视频-长播] <|video_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> +【2026-01-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:24 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-31】 + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + --:-- [商品-加购] <|prod_begin|> + 05:05 [视频-长播] <|video_begin|> + 07:44 [视频-长播] <|video_begin|> + 07:44 [视频-长播] <|video_begin|> + 07:51 [广告-深度转化] <|ad_begin|> + 07:51 [广告-点击] <|ad_begin|> + 07:56 [广告-点击] <|ad_begin|> + 13:03 [视频-长播] <|video_begin|> + 14:55 [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|> + 15:01 [视频-关注/长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:11 [视频-长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> +【2026-02-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:02 [视频-点赞/长播] <|video_begin|> + 05:27 [广告-点击] <|ad_begin|> + 05:30 [广告-点击] <|ad_begin|> +【2026-02-02】 + 02:59 [视频-长播] <|video_begin|> + 03:13 [视频-长播] <|video_begin|> + 03:23 [广告-深度转化] <|ad_begin|> + 03:23 [广告-点击] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:16 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> + 16:46 [视频-长播] <|video_begin|> +【2026-02-03】 + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-点赞/长播] <|video_begin|> + 04:30 [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|> + 08:45 [视频-长播] <|video_begin|> + 09:16 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + 14:30 [视频-关注/点赞/长播] <|video_begin|> +【2026-02-04】 + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-关注/长播] <|video_begin|> + 02:55 [视频-长播] <|video_begin|> + 06:28 [广告-点击] <|ad_begin|> + 06:29 [广告-深度转化] <|ad_begin|> + 06:29 [广告-点击] <|ad_begin|> + 06:31 [广告-深度转化] <|ad_begin|> + 06:31 [广告-点击] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> + 06:34 [广告-深度转化] <|ad_begin|> + 06:34 [广告-点击] <|ad_begin|> + 06:37 [广告-深度转化] <|ad_begin|> + 06:37 [广告-点击] <|ad_begin|> + 06:38 [广告-深度转化] <|ad_begin|> + 06:38 [广告-点击] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 06:40 [广告-点击] <|ad_begin|> + 06:46 [广告-深度转化] <|ad_begin|> + 06:46 [广告-点击] <|ad_begin|> + 06:47 [广告-点击] <|ad_begin|> + 06:47 [广告-深度转化] <|ad_begin|> + 06:47 [广告-点击] <|ad_begin|> + 11:27 [视频-长播] <|video_begin|> + 11:27 [视频-长播] <|video_begin|> + 11:27 [视频-关注/长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 12:25 [广告-点击] <|ad_begin|> + 12:26 [广告-点击] <|ad_begin|> + 15:25 [视频-长播] <|video_begin|> + 15:25 [视频-长播] <|video_begin|> + 16:26 [视频-长播] <|video_begin|> + 16:26 [视频-长播] <|video_begin|> + 16:26 [视频-长播] <|video_begin|> + 16:26 [视频-点赞/长播] <|video_begin|> + 16:35 [视频-长播] <|video_begin|> + 16:41 [视频-长播] <|video_begin|> + 16:41 [视频-长播] <|video_begin|> + 16:41 [视频-长播] <|video_begin|> + 16:41 [视频-关注/长播] <|video_begin|> + 16:47 [视频-关注/长播] <|video_begin|> +【2026-02-05】 + 01:30 [视频-长播] <|video_begin|> + 02:51 [广告-深度转化] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 02:54 [广告-点击] <|ad_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 02:56 [广告-点击] <|ad_begin|> + 02:59 [广告-点击] <|ad_begin|> + 03:01 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:19 [视频-长播] <|video_begin|> + 12:49 [广告-深度转化] <|ad_begin|> + 12:49 [广告-点击] <|ad_begin|> + 12:50 [广告-点击] <|ad_begin|> + 12:54 [广告-深度转化] <|ad_begin|> + 12:54 [广告-点击] <|ad_begin|> + 12:55 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 12:58 [广告-点击] <|ad_begin|> + 13:02 [广告-点击] <|ad_begin|> + 13:03 [广告-深度转化] <|ad_begin|> + 13:03 [广告-点击] <|ad_begin|> + 13:04 [广告-点击] <|ad_begin|> + 13:09 [广告-深度转化] <|ad_begin|> + 13:09 [广告-点击] <|ad_begin|> + 13:10 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:14 [广告-深度转化] <|ad_begin|> + 13:14 [广告-点击] <|ad_begin|> + 13:15 [广告-点击] <|ad_begin|> + +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家的身份，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：从汽车消费意向到新年祈福装饰的关联决策 + +输出格式要求：请仅输出包含 SID 的 JSON 数组，严禁输出任何额外的解释、说明或 Markdown 标记。<|im_end|> +<|im_start|>assistant + + +["<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|living_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|ad_begin|>", "<|prod_begin|>", "<|video_begin|>", "<|prod_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-01-29】 + --:-- [直播-关注] <|living_begin|> +【2025-01-30】 + --:-- [直播-关注] <|living_begin|> +【2025-02-01】 + --:-- [直播-关注] <|living_begin|> +【2025-02-02】 + --:-- [直播-关注] <|living_begin|> +【2025-02-03】 + --:-- [直播-关注] <|living_begin|> +【2025-02-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-03-04】 + --:-- [直播-关注] <|living_begin|> +【2025-03-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-06】 + --:-- [直播-关注] <|living_begin|> +【2025-03-14】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-24】 + --:-- [直播-关注] <|living_begin|> +【2025-03-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-04】 + --:-- [直播-关注] <|living_begin|> +【2025-04-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-30】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-02】 + --:-- [直播-关注] <|living_begin|> +【2025-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-03】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-24】 + --:-- [直播-关注] <|living_begin|> +【2025-08-27】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-09】 + 11:07 [广告-深度转化] <|ad_begin|> +【2025-12-10】 + 09:55 [广告-深度转化] <|ad_begin|> + 14:51 [广告-深度转化] <|ad_begin|> +【2025-12-12】 + 02:13 [广告-深度转化] <|ad_begin|> + 09:03 [广告-深度转化] <|ad_begin|> + 12:12 [广告-深度转化] <|ad_begin|> + 17:18 [广告-深度转化] <|ad_begin|> +【2025-12-13】 + 11:22 [广告-深度转化] <|ad_begin|> +【2025-12-14】 + 04:36 [广告-深度转化] <|ad_begin|> + 10:08 [广告-深度转化] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> + 13:06 [广告-深度转化] <|ad_begin|> + 15:17 [广告-深度转化] <|ad_begin|> +【2025-12-15】 + 00:16 [广告-深度转化] <|ad_begin|> + 13:23 [广告-深度转化] <|ad_begin|> + 15:08 [广告-深度转化] <|ad_begin|> + 23:06 [广告-深度转化] <|ad_begin|> +【2025-12-16】 + 02:51 [广告-深度转化] <|ad_begin|> + 04:39 [广告-深度转化] <|ad_begin|> + 06:48 [广告-深度转化] <|ad_begin|> + 10:33 [广告-深度转化] <|ad_begin|> + 13:03 [广告-深度转化] <|ad_begin|> + 13:21 [广告-深度转化] <|ad_begin|> + 15:03 [广告-深度转化] <|ad_begin|> + 15:52 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + 03:28 [广告-深度转化] <|ad_begin|> + 09:46 [广告-深度转化] <|ad_begin|> + 10:55 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + 10:03 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + 10:05 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 07:44 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 07:49 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 04:42 [广告-点击] <|ad_begin|> + 09:49 [广告-点击] <|ad_begin|> + 13:23 [广告-点击] <|ad_begin|> + 20:33 [广告-点击] <|ad_begin|> + 20:35 [广告-点击] <|ad_begin|> + 20:36 [广告-点击] <|ad_begin|> + 20:37 [广告-点击] <|ad_begin|> + 20:40 [广告-点击] <|ad_begin|> + 21:27 [广告-点击] <|ad_begin|> +【2026-01-20】 + 16:09 [广告-点击] <|ad_begin|> + 20:56 [广告-点击] <|ad_begin|> + 20:58 [广告-点击] <|ad_begin|> + 21:00 [广告-点击] <|ad_begin|> + 21:09 [广告-点击] <|ad_begin|> + 22:23 [广告-点击] <|ad_begin|> +【2026-01-22】 + 08:21 [广告-点击] <|ad_begin|> + 14:54 [广告-点击] <|ad_begin|> + 20:38 [广告-点击] <|ad_begin|> + 20:40 [广告-点击] <|ad_begin|> + 20:43 [广告-点击] <|ad_begin|> + 21:30 [广告-点击] <|ad_begin|> + 21:31 [广告-点击] <|ad_begin|> +【2026-01-23】 + 16:33 [广告-点击] <|ad_begin|> + 22:25 [广告-点击] <|ad_begin|> + 22:28 [广告-点击] <|ad_begin|> +【2026-01-24】 + 05:01 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 04:34 [广告-点击] <|ad_begin|> + 04:35 [广告-点击] <|ad_begin|> + 20:42 [广告-点击] <|ad_begin|> + 20:43 [广告-点击] <|ad_begin|> + 20:45 [广告-点击] <|ad_begin|> +【2026-01-26】 + 21:11 [广告-点击] <|ad_begin|> + 21:15 [广告-点击] <|ad_begin|> + 22:04 [广告-点击] <|ad_begin|> + 22:13 [广告-点击] <|ad_begin|> + 22:18 [广告-点击] <|ad_begin|> + 22:22 [广告-点击] <|ad_begin|> +【2026-01-28】 + 02:35 [广告-点击] <|ad_begin|> + 13:29 [视频-浏览] <|video_begin|> + 20:54 [广告-点击] <|ad_begin|> +【2026-01-29】 + 14:01 [广告-点击] <|ad_begin|> + 15:58 [广告-点击] <|ad_begin|> + 16:14 [广告-点击] <|ad_begin|> + 16:18 [广告-点击] <|ad_begin|> + 16:23 [广告-点击] <|ad_begin|> + 21:04 [广告-点击] <|ad_begin|> + 22:03 [广告-点击] <|ad_begin|> + 22:19 [广告-点击] <|ad_begin|> + 22:24 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [商品-购买] <|prod_begin|> + 21:00 [广告-点击] <|ad_begin|> +【2026-02-01】 + 21:19 [广告-点击] <|ad_begin|> + 22:11 [广告-点击] <|ad_begin|> + 22:18 [广告-点击] <|ad_begin|> +【2026-02-02】 + 04:26 [广告-点击] <|ad_begin|> + 04:41 [广告-点击] <|ad_begin|> +【2026-02-03】 + 03:56 [广告-点击] <|ad_begin|> + 04:02 [广告-点击] <|ad_begin|> + 07:43 [视频-收藏/长播] <|video_begin|> + 13:20 [广告-点击] <|ad_begin|> + 21:00 [广告-点击] <|ad_begin|> + 21:02 [广告-点击] <|ad_begin|> + 21:07 [广告-点击] <|ad_begin|> + 21:09 [广告-点击] <|ad_begin|> + 21:15 [广告-点击] <|ad_begin|> + 22:04 [广告-点击] <|ad_begin|> + 22:25 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [直播-关注] <|living_begin|> + 02:27 [视频-关注/长播] <|video_begin|> + 02:27 [视频-关注/长播] <|video_begin|> + 06:04 [广告-点击] <|ad_begin|> + 09:00 [视频-关注/长播] <|video_begin|> + 10:42 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [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|> + 21:15 [广告-点击] <|ad_begin|> + 21:23 [广告-点击] <|ad_begin|> + 21:25 [广告-点击] <|ad_begin|> + 21:28 [广告-点击] <|ad_begin|> + 21:32 [广告-点击] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> +【2026-02-05】 + 05:32 [视频-浏览] <|video_begin|> + 11:44 [视频-长播] <|video_begin|> + 11:44 [视频-长播] <|video_begin|> + 11:44 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:50 [视频-长播] <|video_begin|> + 11:50 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 11:55 [视频-长播] <|video_begin|> + 12:39 [视频-长播] <|video_begin|> + 12:39 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 12:40 [视频-长播] <|video_begin|> + 12:41 [视频-长播] <|video_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:42 [视频-长播] <|video_begin|> + 12:44 [视频-长播] <|video_begin|> + 12:44 [视频-长播] <|video_begin|> + 12:45 [视频-长播] <|video_begin|> + 12:45 [视频-长播] <|video_begin|> + 12:48 [视频-长播] <|video_begin|> + 12:48 [视频-长播] <|video_begin|> + 12:48 [视频-长播] <|video_begin|> + 12:49 [视频-长播] <|video_begin|> + 12:49 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:55 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:56 [视频-长播] <|video_begin|> + 12:58 [视频-长播] <|video_begin|> + 12:58 [视频-长播] <|video_begin|> + 12:59 [视频-长播] <|video_begin|> + 12:59 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:10 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:24 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:32 [视频-长播] <|video_begin|> + 13:33 [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-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-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：你是一名极端严苛的用户行为数据挖掘专家，同时具备数据格式化能力，请根据以上用户交互历史，针对指定主题提取具有行为递进逻辑及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）： 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 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在同一主题类目中，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：从被动接收到主动探索：春节年货的筹备与实践 + +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：春节年货的筹备与实践", + "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" + } + ] + } +}<|im_end|> +<|im_start|>assistant + +好的，我将以推荐分析师的视角，对这位用户的行为进行推理。 + +我需要分析的行为主题是用户如何从被动接收信息，逐步转向主动探索，并最终完成春节年货的筹备与实践。 + +首先，我注意到用户的行为时间线在2026年2月5日，也就是临近春节的时候，出现了一系列密集的、与烹饪和家居相关的互动。这表明用户的关注点开始向节日筹备转移。 + +我的分析起点是用户在当天观看的一个长视频 <|video_begin|>。这个视频内容是关于太原老葛陈年花雕料酒的，视频中明确提到了“春节期间推出促销活动”以及料酒在烹饪中“去腥增香”的特点。我认为，这个视频很可能是用户筹备年货的兴趣触发点。它并非用户主动搜索的结果，而是在浏览中被动接收到的信息，但内容恰好契合了节日的潜在需求，成功地将用户的注意力引导至烹饪调味品上。 + +紧接着，我发现用户几乎是立刻就将这种被动接收的兴趣转化为了主动行为。他点击了一个商品 <|prod_begin|>，这个商品正是“六年陈花雕料酒”，并且规格是“5斤一桶”。这说明视频内容成功激发了用户的购买意图，他开始主动了解商品的具体参数，比如年份和容量，这是从被动浏览到主动决策的关键一步。 + +用户的思维显然没有停留在单一的调味品上。在点击了料酒之后，他的注意力迅速扩展到了整个春节用餐场景。我看到他紧接着点击了一套餐具 <|prod_begin|>，这套餐具的名称带有鲜明的节日色彩——“年年有余”，并且是包含6碗4盘的10件套。这个行为非常有逻辑性，说明用户已经开始系统性地构思节日聚餐的整体氛围和体验，认知从准备单一的菜品，扩展到了提升整个用餐的仪式感。 + +最后，用户的行为链条从构思和准备，走向了具体的实践落地。在浏览了调味品和餐具之后，他最终的购买决策落在了两台绞肉机上 <|prod_begin|>。购买厨房小家电，尤其是绞肉机这种在节日期间常用于制作饺子馅、丸子等菜肴的工具，是一个非常明确的信号。这表明用户已经从“想做什么菜”的构思阶段，进入了“要用什么工具来做”的执行阶段。这个购买行为将前面对于料酒和餐具的兴趣收敛到了具体的烹饪实践上，形成了一个从“看”到“想”再到“做”的完整闭环。 + +综上所述，我认为用户的行为逻辑链条清晰地展现了从被动接收信息到主动探索并最终实践的过程。整个链条始于一个不经意间看到的料酒视频 <|video_begin|>，这激发了他对年货的初步兴趣；随后他主动点击查看了同款料酒 <|prod_begin|>，将兴趣转化为具体的商品研究；接着，他的需求从单一商品扩展到整个春节用餐场景，点击了“年年有余”主题餐具 <|prod_begin|>；最终，他为了将烹饪构想付诸实践，购买了作为核心工具的绞肉机 <|prod_begin|>。这一系列行为逻辑紧密，层层递进，未来他可能偏好与家庭烹饪、厨房小家电以及节日装饰相关的推荐内容。 +{"logic_chain": {"name": "从被动接收到主动探索：春节年货的筹备与实践", "events": [{"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣触发：被动观看到关于“陈年花雕料酒”用于烹饪的视频，内容强调“去腥增香”和“春节促销”，为后续的采购行为埋下伏笔。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "行为转化与参数对比：观看视频后，立即主动点击了与视频内容高度一致的“陈年花雕料酒”商品，并开始关注具体参数如“六年陈”、“5斤一桶”，体现了从被动接收信息到主动决策的转变。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "需求扩展：在准备核心调味品后，用户的兴趣扩展到与“春节”场景强相关的“年年有余”主题餐具，表明其正在为节日聚餐进行系统性准备，认知从单一食材扩展到整体用餐体验。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "实践落地与工具升级：最终购买了绞肉机，这是典型的烹饪实践准备行为。结合前序对料酒、餐具的关注，表明用户已从“构思”进入“执行”阶段，其需求从食材、餐具收敛到了具体的厨房工具，完成了从“看”到“做”的闭环。"}]}}<|im_end|> +<|im_start|>user +你负责根据用户多域行为理解用户兴趣偏好，并输出该用户在各场景中的目标内容。 +下面给出用户历史行为线索： +广告行为: 点击过广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>。 + +请基于这些线索总结该用户在各场景中的目标内容。<|im_end|> +<|im_start|>assistant +好的，遵照您的指示，这是基于用户历史行为的兴趣偏好分析与内容预测： + +### 【兴趣归纳】 +根据用户的多域行为记录，可以归纳出以下几个核心兴趣类目： + +1. **悬疑/志怪类内容**：这是用户最核心且交互最深的兴趣。体现在用户在2026年1月17日至1月25日期间，通过多次(合计超过28次)高频搜索，主动查找特定创作者发布的悬疑、伪人危机、灾难体验等主题的系列化视频内容。这种主动、重复的搜索行为表明了其对此类内容的极高黏性与追更意愿。 + +2. **网络热门创作者与流行文化**：用户对互联网流行文化及特定内容创作者表现出浓厚兴趣。在2026年1月初，用户连续三天分别搜索了三位风格各异的热门创作者(搜索次数分别为3次、5次、4次)，显示出其关注网络热点、追踪创作者动态的习惯，交互深度较高。 + +3. **实用生活与数字消费**：此兴趣主要通过广告点击行为体现。用户点击了涉及平台促销<|ad_begin|>、移动支付<|ad_begin|>、生活常识<|ad_begin|>及科技产品预告(搜索次数7次)的内容。这类兴趣的交互深度为中等，表现为机会驱动下的点击和短期关注，而非持续性追随。 + +### 【行为模式】 +用户的行为模式呈现出“主动探索”与“被动发现”相结合的特点。 + +**消费偏好上**，用户具有明确的目标导向性。当对某个主题(尤其是悬疑类)或创作者产生兴趣后，会通过高频、精准的搜索来获取完整或最新的内容，表现出对系列化、有连续剧情内容的偏爱，以及较高的用户忠诚度。 + +**跨域协同信号上**，用户的搜索行为与广告点击行为存在一定的关联性。例如，用户对数字支付、手机应用的广告点击(<|ad_begin|>、<|ad_begin|>)，与其主动搜索科技产品信息的行为相呼应，共同构成了“数字生活”的兴趣画像。此外，2026年1月17日点击的包含“神秘氛围”与“夜间场景”的广告<|ad_begin|>，可能在情绪上预热或强化了用户后续对悬疑内容的强烈兴趣，形成了跨域兴趣的接力。 + +### 【预测总结】 +综合分析，预测用户接下来最可能感兴趣的内容是**具有神秘感、互动性强的新人创作者直播或视频**。 + +推断依据如下： +首先，用户对悬疑、神秘主题的深度迷恋是其最稳固的兴趣核心。其次，用户有主动发掘并追随特定创作者的习惯，显示出对“人”的关注超过了对单纯“内容”的关注。最后，其行为模式表明他乐于探索新奇内容。将这几点结合，相比于已经成熟的、高度制作化的视频，一个正在成长、内容尚在探索期、带有神秘气质(如不露脸)且能提供实时文字/声音互动的新人创作者，很可能满足用户对“神秘感”、“内容发现”和“建立连接”的多重心理需求。因此，推荐此类直播或视频内容，有较大潜力获得用户的点击乃至深度参与。 +该用户最近首次打赏了主播: <|living_begin|><|im_end|> +<|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, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -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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<|video_begin|>。收藏这个行为对我来说是一个强烈的信号，它意味着用户不再满足于信息的简单浏览，而是希望对复杂的决策逻辑进行深度思考和复盘。我推测，面对海量且充满不确定性的财经信息，他可能感到了认知上的压力和决策上的痛点，从而萌生了寻找更高效信息处理方法或决策辅助工具的想法。 + +这个推测很快在接下来的行为中得到了印证。仅仅三天后，在2026年1月29日，他首次主动点击了一则关于免费人工智能课程的广告 <|ad_begin|>。这是一个非常重要的行为变化，标志着他开始将注意力从“信息本身”转移到了“处理信息的工具”上。他不再仅仅是被动地接收财经博主的观点，而是开始主动探索能够赋能自己的新技术。这说明他已经意识到，AI可能就是解决他当前信息处理瓶颈的潜在方案。 + +为了进一步确认他对“工具”和“效率”的追求，我继续观察后续行为。在2026年2月1日，他又收藏了一个关于荣耀手机七大实用功能的视频 <|video_begin|>，内容聚焦于如何通过手机自带的功能（如分屏、长截屏）来提升操作效率。这个行为虽然看似与AI没有直接关联，但其内在动机与探索AI工具是高度一致的，都指向了“利用工具提升个人效率”这一核心诉求。这表明他的认知已经发生了转变，从一个纯粹的内容消费者，开始向一个积极寻求并利用工具来优化个人工作流的“效率追求者”演变。 + +综合来看，我认为用户的行为逻辑链条清晰地展现了一个认知跃迁的过程。他最初通过大量观看财经、时政类视频积累信息，在信息过载中遇到了决策瓶颈，这具体体现在他收藏了关于投资策略与心理学研究的视频 <|video_begin|>上。这个痛点驱动他开始寻找解决方案，于是他主动点击了免费人工智能课程的广告 <|ad_begin|>，迈出了探索新工具的第一步。最后，他收藏手机高效操作技巧的视频 <|video_begin|>，进一步巩固了他对于利用工具提升效率的认知和兴趣。因此，我倾向于认为，该用户未来对各类能提升信息处理效率、辅助决策的AI工具或相关课程内容，会有较高的接受度和付费转化可能。 +{"logic_chain": {"name": "从被动信息吸收到主动利用AI工具的认知跃迁", "events": [{"date": "2026-01-26", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "信息积累与痛点形成：用户通过大量观看财经、科技、时政类视频，积累了海量信息，并收藏了关于“投资策略与心理学”的深度分析视频，表明其在信息处理和决策上可能遇到了瓶颈，产生了对更高效工具的需求。"}, {"date": "2026-01-29", "action": "[广告-点击] <|ad_begin|>", "logic": "兴趣转向与工具探索：用户开始主动点击“免费人工智能课程”广告，标志着其兴趣从被动接收信息转向主动学习和了解能提升信息处理效率的AI工具，行为模式发生显著变化。"}, {"date": "2026-02-01", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "认知深化与效率追求：用户收藏了关于“手机实用功能”以提升操作效率的视频。这一行为与探索AI工具的逻辑一致，共同指向其核心诉求——利用工具提升个人效率，进一步佐证了其从“信息消费”到“工具赋能”的认知转变。"}]}}<|im_end|> + + +["<|ad_begin|>", "<|ad_begin|>", "<|living_begin|>", "<|living_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>", "<|video_begin|>"]<|im_end|> + + +["<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|living_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|ad_begin|>", "<|prod_begin|>", "<|video_begin|>", "<|prod_begin|>"]<|im_end|> + +好的，我将以推荐分析师的视角，对这位用户的行为进行推理。 + +我需要分析的行为主题是用户如何从被动接收信息，逐步转向主动探索，并最终完成春节年货的筹备与实践。 + +首先，我注意到用户的行为时间线在2026年2月5日，也就是临近春节的时候，出现了一系列密集的、与烹饪和家居相关的互动。这表明用户的关注点开始向节日筹备转移。 + +我的分析起点是用户在当天观看的一个长视频 <|video_begin|>。这个视频内容是关于太原老葛陈年花雕料酒的，视频中明确提到了“春节期间推出促销活动”以及料酒在烹饪中“去腥增香”的特点。我认为，这个视频很可能是用户筹备年货的兴趣触发点。它并非用户主动搜索的结果，而是在浏览中被动接收到的信息，但内容恰好契合了节日的潜在需求，成功地将用户的注意力引导至烹饪调味品上。 + +紧接着，我发现用户几乎是立刻就将这种被动接收的兴趣转化为了主动行为。他点击了一个商品 <|prod_begin|>，这个商品正是“六年陈花雕料酒”，并且规格是“5斤一桶”。这说明视频内容成功激发了用户的购买意图，他开始主动了解商品的具体参数，比如年份和容量，这是从被动浏览到主动决策的关键一步。 + +用户的思维显然没有停留在单一的调味品上。在点击了料酒之后，他的注意力迅速扩展到了整个春节用餐场景。我看到他紧接着点击了一套餐具 <|prod_begin|>，这套餐具的名称带有鲜明的节日色彩——“年年有余”，并且是包含6碗4盘的10件套。这个行为非常有逻辑性，说明用户已经开始系统性地构思节日聚餐的整体氛围和体验，认知从准备单一的菜品，扩展到了提升整个用餐的仪式感。 + +最后，用户的行为链条从构思和准备，走向了具体的实践落地。在浏览了调味品和餐具之后，他最终的购买决策落在了两台绞肉机上 <|prod_begin|>。购买厨房小家电，尤其是绞肉机这种在节日期间常用于制作饺子馅、丸子等菜肴的工具，是一个非常明确的信号。这表明用户已经从“想做什么菜”的构思阶段，进入了“要用什么工具来做”的执行阶段。这个购买行为将前面对于料酒和餐具的兴趣收敛到了具体的烹饪实践上，形成了一个从“看”到“想”再到“做”的完整闭环。 + +综上所述，我认为用户的行为逻辑链条清晰地展现了从被动接收信息到主动探索并最终实践的过程。整个链条始于一个不经意间看到的料酒视频 <|video_begin|>，这激发了他对年货的初步兴趣；随后他主动点击查看了同款料酒 <|prod_begin|>，将兴趣转化为具体的商品研究；接着，他的需求从单一商品扩展到整个春节用餐场景，点击了“年年有余”主题餐具 <|prod_begin|>；最终，他为了将烹饪构想付诸实践，购买了作为核心工具的绞肉机 <|prod_begin|>。这一系列行为逻辑紧密，层层递进，未来他可能偏好与家庭烹饪、厨房小家电以及节日装饰相关的推荐内容。 +{"logic_chain": {"name": "从被动接收到主动探索：春节年货的筹备与实践", "events": [{"date": "2026-02-05", "action": "[视频-长播] <|video_begin|>", "logic": "兴趣触发：被动观看到关于“陈年花雕料酒”用于烹饪的视频，内容强调“去腥增香”和“春节促销”，为后续的采购行为埋下伏笔。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "行为转化与参数对比：观看视频后，立即主动点击了与视频内容高度一致的“陈年花雕料酒”商品，并开始关注具体参数如“六年陈”、“5斤一桶”，体现了从被动接收信息到主动决策的转变。"}, {"date": "2026-02-05", "action": "[商品-点击] <|prod_begin|>", "logic": "需求扩展：在准备核心调味品后，用户的兴趣扩展到与“春节”场景强相关的“年年有余”主题餐具，表明其正在为节日聚餐进行系统性准备，认知从单一食材扩展到整体用餐体验。"}, {"date": "2026-02-05", "action": "[商品-购买] <|prod_begin|>", "logic": "实践落地与工具升级：最终购买了绞肉机，这是典型的烹饪实践准备行为。结合前序对料酒、餐具的关注，表明用户已从“构思”进入“执行”阶段，其需求从食材、餐具收敛到了具体的厨房工具，完成了从“看”到“做”的闭环。"}]}}<|im_end|> +好的，遵照您的指示，这是基于用户历史行为的兴趣偏好分析与内容预测： + +### 【兴趣归纳】 +根据用户的多域行为记录，可以归纳出以下几个核心兴趣类目： + +1. **悬疑/志怪类内容**：这是用户最核心且交互最深的兴趣。体现在用户在2026年1月17日至1月25日期间，通过多次(合计超过28次)高频搜索，主动查找特定创作者发布的悬疑、伪人危机、灾难体验等主题的系列化视频内容。这种主动、重复的搜索行为表明了其对此类内容的极高黏性与追更意愿。 + +2. **网络热门创作者与流行文化**：用户对互联网流行文化及特定内容创作者表现出浓厚兴趣。在2026年1月初，用户连续三天分别搜索了三位风格各异的热门创作者(搜索次数分别为3次、5次、4次)，显示出其关注网络热点、追踪创作者动态的习惯，交互深度较高。 + +3. **实用生活与数字消费**：此兴趣主要通过广告点击行为体现。用户点击了涉及平台促销<|ad_begin|>、移动支付<|ad_begin|>、生活常识<|ad_begin|>及科技产品预告(搜索次数7次)的内容。这类兴趣的交互深度为中等，表现为机会驱动下的点击和短期关注，而非持续性追随。 + +### 【行为模式】 +用户的行为模式呈现出“主动探索”与“被动发现”相结合的特点。 + +**消费偏好上**，用户具有明确的目标导向性。当对某个主题(尤其是悬疑类)或创作者产生兴趣后，会通过高频、精准的搜索来获取完整或最新的内容，表现出对系列化、有连续剧情内容的偏爱，以及较高的用户忠诚度。 + +**跨域协同信号上**，用户的搜索行为与广告点击行为存在一定的关联性。例如，用户对数字支付、手机应用的广告点击(<|ad_begin|>、<|ad_begin|>)，与其主动搜索科技产品信息的行为相呼应，共同构成了“数字生活”的兴趣画像。此外，2026年1月17日点击的包含“神秘氛围”与“夜间场景”的广告<|ad_begin|>，可能在情绪上预热或强化了用户后续对悬疑内容的强烈兴趣，形成了跨域兴趣的接力。 + +### 【预测总结】 +综合分析，预测用户接下来最可能感兴趣的内容是**具有神秘感、互动性强的新人创作者直播或视频**。 + +推断依据如下： +首先，用户对悬疑、神秘主题的深度迷恋是其最稳固的兴趣核心。其次，用户有主动发掘并追随特定创作者的习惯，显示出对“人”的关注超过了对单纯“内容”的关注。最后，其行为模式表明他乐于探索新奇内容。将这几点结合，相比于已经成熟的、高度制作化的视频，一个正在成长、内容尚在探索期、带有神秘气质(如不露脸)且能提供实时文字/声音互动的新人创作者，很可能满足用户对“神秘感”、“内容发现”和“建立连接”的多重心理需求。因此，推荐此类直播或视频内容，有较大潜力获得用户的点击乃至深度参与。 +该用户最近首次打赏了主播: <|living_begin|><|im_end|> +[INFO|configuration_utils.py:771] 2026-07-06 02:48:24,430 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-06 02:48:24,431 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +[INFO|2026-07-06 02:48:24] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-06 02:48:24] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-06 02:48:25,182 >> loading weights file model.safetensors from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-06 02:48:25,182 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-06 02:48:25,233 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file generation_config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-06 02:48:26,035 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-06 02:48:26,485 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-06 02:48:26] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-06 02:48:26] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-06 02:48:26] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-06 02:48:26] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-06 02:48:26] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-06 02:48:26,509 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}. +[INFO|trainer.py:1469] 2026-07-06 02:48:26,618 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-06 02:48:26,618 >> Num examples = 1,597 +[INFO|trainer.py:1471] 2026-07-06 02:48:26,618 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-06 02:48:26,618 >> Num update steps per epoch = 400 +[INFO|trainer.py:1473] 2026-07-06 02:48:26,618 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-06 02:48:26,618 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-06 02:48:26,618 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-06 02:48:26,618 >> Total optimization steps = 400 +[INFO|trainer.py:1479] 2026-07-06 02:48:26,618 >> Number of trainable parameters = 801,433,600 + 0%| | 0/400 [00:00> `use_return_dict` is deprecated! 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model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/checkpoint-400/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 03:41:51,455 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/checkpoint-400/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 03:41:51,476 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/checkpoint-400/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-06 03:41:53,154 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 400/400 [53:26<00:00, 5.98s/it] 100%|██████████| 400/400 [53:26<00:00, 8.02s/it] +[INFO|trainer.py:3815] 2026-07-06 03:41:53,155 >> Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5 +[INFO|configuration_utils.py:533] 2026-07-06 03:41:53,156 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/config.json +[INFO|configuration_utils.py:816] 2026-07-06 03:41:53,156 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/generation_config.json +{'train_runtime': '3207', 'train_samples_per_second': '0.498', 'train_steps_per_second': '0.125', 'train_loss': '1.568', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 03:41:53,953 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 03:41:53,974 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 178221581GF + train_loss = 1.5678 + train_runtime = 0:53:26.53 + train_samples_per_second = 0.498 + train_steps_per_second = 0.125 +Figure saved at: /home/ll/llm4rec/experiments/20260706_overnight/outputs/v06_no_think_input_lr2e5/training_loss.png +[WARNING|2026-07-06 03:41:54] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-06 03:41:54] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-06 03:41:54,081 >> 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..790f83d --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.913639661702574e+17, + "train_loss": 1.5678150677680969, + "train_runtime": 3206.5361, + "train_samples_per_second": 0.498, + "train_steps_per_second": 0.125 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..9ccd3f7 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,81 @@ +{"current_steps": 5, "total_steps": 400, "loss": 2.8921972274780274, "lr": 6.666666666666667e-06, "epoch": 0.012523481527864746, "percentage": 1.25, "elapsed_time": "0:00:41", "remaining_time": "0:54:48"} +{"current_steps": 10, "total_steps": 400, "loss": 2.628790283203125, "lr": 1.5000000000000002e-05, "epoch": 0.025046963055729492, "percentage": 2.5, "elapsed_time": 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