From 554c7f1188f206feaf8ee2063542e911b1a0e13c Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 16 Aug 2026 18:03:17 +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/v08_rec_focus_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 + ...82650.ubuntu-System-Product-Name.1677645.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 978 ++++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 56 + trainer_state.json | 428 ++++++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 36944 bytes 16 files changed, 1764 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_04-17-30_ubuntu-System-Product-Name/events.out.tfevents.1783282650.ubuntu-System-Product-Name.1677645.0 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..1951423 --- /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: v08_rec_focus_lr2e5 + results: [] +--- + + + +# v08_rec_focus_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_rec_focus 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: 202607068 +- 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..f1232c5 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.335043548266926e+17, + "train_loss": 1.5747115251829298, + "train_runtime": 2170.5286, + "train_samples_per_second": 0.512, + "train_steps_per_second": 0.128 +} \ 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..144c10a --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acd27da718075ff3ab7a5664c97044c138a235072a04b68af3f71336d5d140b8 +size 1602902832 diff --git a/runs/Jul06_04-17-30_ubuntu-System-Product-Name/events.out.tfevents.1783282650.ubuntu-System-Product-Name.1677645.0 b/runs/Jul06_04-17-30_ubuntu-System-Product-Name/events.out.tfevents.1783282650.ubuntu-System-Product-Name.1677645.0 new file mode 100644 index 0000000..893a979 --- /dev/null +++ b/runs/Jul06_04-17-30_ubuntu-System-Product-Name/events.out.tfevents.1783282650.ubuntu-System-Product-Name.1677645.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7e744e61372cf82f02b676403e82dec5883f3bd1d38fada3c64d7390c68a44d +size 17819 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..b1d371b --- /dev/null +++ b/train.log @@ -0,0 +1,978 @@ +/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 04:16:59] 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 04:17:00,101 >> 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 04:17:00,103 >> 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 04:17:03,864 >> 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 04:17:03,865 >> 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 04:17:04,245 >> 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 04:17:04,246 >> 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 04:17:05] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/20260706_overnight/data/rec_focus.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: 9631 examples [00:00, 85315.66 examples/s] Generating train split: 19204 examples [00:00, 91479.90 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/19204 [00:00user +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。 +请阅读下面的用户行为记录，并推断该用户在各推荐场景中的目标内容。 + +用户购物行为: 浏览了商品 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<|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/点赞/长播行为，看过视频 <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/点赞/长播行为，点赞过 <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/收藏/长播行为，对视频 <|video_begin|> 有过转发/收藏/点赞/长播行为。 +广告行为: 对广告 <|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|>, <|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|> 完成过深度转化，点击过广告 <|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|>, <|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|>。/think<|im_end|> +<|im_start|>assistant +好的，遵照您的指示，以下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】 +根据用户的多域交互历史，可以归纳出其核心兴趣主要集中在以下四个方面： +1. **剧情内容与情感故事**：这是用户近期最核心且互动最深的兴趣。自2025年12月起，用户对以短剧、网文小说为代表的剧情内容表现出极高热情。其行为包括对大量此类广告的深度转化<|ad_begin|>, <|ad_begin|>等)，以及在搜索域进行高频、重复的主动探索(如对特定剧情设定的7次和5次搜索)。内容主题多为家庭矛盾、情感纠葛、职场逆袭、古风宫斗等强戏剧性元素。 +2. **女性时尚与个护美妆**：此为用户长期且稳定的消费兴趣。用户在商品域有多次购买(`商品-购买`)和大量点击(`商品-点击`)行为，覆盖了从服饰(特别是强调“大码”、“显瘦”、“塑形”功能的款式)、内衣、鞋包到美妆护肤(面霜、精华、洗发水、唇膏等)及个护(女性清洗液、卫生巾)的全链路。近期对羽绒服、大衣等冬季服饰有密集的点击行为。 +3. **居家生活与美食**：用户对此类目表现出高频的消费和浏览行为。购买记录(`商品-购买`)横跨休闲零食、代餐麦片、蛋糕烘焙、生鲜水果等多个品类，显示出对即食和健康食品的偏好。近期对车厘子、蓝莓等高客单价水果有大量点击，并对烹饪、手工DIY、收纳整理等视频内容有收藏、点赞等深度互动<|video_begin|>。 +4. **娱乐八卦与社会热点**：这是一个泛兴趣领域，用户通过主动搜索和视频点赞表现出关注。内容涉及明星艺人、网红动态、影视剧集(如《甄嬛传》)的二次创作以及社会事件等。互动深度中等，主要为浏览、点赞和搜索行为。 + +【行为模式】 +用户的消费偏好呈现出务实与兴趣导向并存的特点。一方面，她会购买“二级品”、“瑕疵款”商品，并关注“省钱技巧”类内容，显示出对高性价比的追求。另一方面，她也会被即时兴趣驱动，大量购买和点击各类零食、水果。其购物行为存在明显的复购周期，如对某品牌面霜<|prod_begin|>和洗发水<|prod_begin|>的多次购买。 + +用户的跨域协同信号非常强烈： +* **内容引导消费(C2E)**：用户在观看羽绒服穿搭<|video_begin|>、车厘子介绍<|video_begin|>等视频后，在商品域产生了大量相关品类的点击行为，展现了从内容种草到消费意向的直接转化路径。 +* **搜索驱动内容消费**：用户在搜索域探索特定小说剧情后，其信息流中显著增加了同类型题材的短剧广告和视频推荐，并产生了大量深度转化，表明搜索行为是其内容消费的强力风向标。 +* **兴趣身份统一**：用户在各域的行为共同刻画了一个关注自我形象、享受生活、追逐流行文化与剧情内容的年轻女性形象，各域兴趣相互印证，高度统一。 + +【预测总结】 +综合分析，推测用户接下来最可能感兴趣并产生点击的内容是**与短剧、网文小说相关的剧情类视频或广告，特别是那些包含“家庭财产纠纷”、“重生逆袭”、“真假千金”等强冲突元素的内容**。这一预测主要基于用户近期在广告、视频和搜索域表现出的极高互动强度和主动探索意愿，该兴趣已成为其当前最核心的关注点。 + +其次，**高品质生鲜水果(如车厘子、榴莲、蓝莓等)的促销信息、产地溯源或美食制作类视频**也可能吸引其点击。这源于她近期在商品域对该品类表现出的强烈关注和浏览行为。 + +此外，考虑到用户持续的消费习惯和季节性需求，**新款的“大码/显瘦”女装、功能性内衣，或其复购过的护肤品牌<|prod_begin|>关联的品牌)的推广内容**，也具备较高的吸引力，可能促使其发生点击或购买行为。 +该用户最近喜欢的视频有: <|video_begin|><|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。 +请阅读下面的用户行为记录，并推断该用户在各推荐场景中的目标内容。 + +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/点赞/长播行为，看过视频 <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/点赞/长播行为，点赞过 <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/收藏/长播行为，对视频 <|video_begin|> 有过转发/收藏/点赞/长播行为。 +广告行为: 对广告 <|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|>, <|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|> 完成过深度转化，点击过广告 <|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|>, <|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|>。/think<|im_end|> +<|im_start|>assistant +好的，遵照您的指示，以下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】 +根据用户的多域交互历史，可以归纳出其核心兴趣主要集中在以下四个方面： +1. **剧情内容与情感故事**：这是用户近期最核心且互动最深的兴趣。自2025年12月起，用户对以短剧、网文小说为代表的剧情内容表现出极高热情。其行为包括对大量此类广告的深度转化<|ad_begin|>, <|ad_begin|>等)，以及在搜索域进行高频、重复的主动探索(如对特定剧情设定的7次和5次搜索)。内容主题多为家庭矛盾、情感纠葛、职场逆袭、古风宫斗等强戏剧性元素。 +2. **女性时尚与个护美妆**：此为用户长期且稳定的消费兴趣。用户在商品域有多次购买(`商品-购买`)和大量点击(`商品-点击`)行为，覆盖了从服饰(特别是强调“大码”、“显瘦”、“塑形”功能的款式)、内衣、鞋包到美妆护肤(面霜、精华、洗发水、唇膏等)及个护(女性清洗液、卫生巾)的全链路。近期对羽绒服、大衣等冬季服饰有密集的点击行为。 +3. **居家生活与美食**：用户对此类目表现出高频的消费和浏览行为。购买记录(`商品-购买`)横跨休闲零食、代餐麦片、蛋糕烘焙、生鲜水果等多个品类，显示出对即食和健康食品的偏好。近期对车厘子、蓝莓等高客单价水果有大量点击，并对烹饪、手工DIY、收纳整理等视频内容有收藏、点赞等深度互动<|video_begin|>。 +4. **娱乐八卦与社会热点**：这是一个泛兴趣领域，用户通过主动搜索和视频点赞表现出关注。内容涉及明星艺人、网红动态、影视剧集(如《甄嬛传》)的二次创作以及社会事件等。互动深度中等，主要为浏览、点赞和搜索行为。 + +【行为模式】 +用户的消费偏好呈现出务实与兴趣导向并存的特点。一方面，她会购买“二级品”、“瑕疵款”商品，并关注“省钱技巧”类内容，显示出对高性价比的追求。另一方面，她也会被即时兴趣驱动，大量购买和点击各类零食、水果。其购物行为存在明显的复购周期，如对某品牌面霜<|prod_begin|>和洗发水<|prod_begin|>的多次购买。 + +用户的跨域协同信号非常强烈： +* **内容引导消费(C2E)**：用户在观看羽绒服穿搭<|video_begin|>、车厘子介绍<|video_begin|>等视频后，在商品域产生了大量相关品类的点击行为，展现了从内容种草到消费意向的直接转化路径。 +* **搜索驱动内容消费**：用户在搜索域探索特定小说剧情后，其信息流中显著增加了同类型题材的短剧广告和视频推荐，并产生了大量深度转化，表明搜索行为是其内容消费的强力风向标。 +* **兴趣身份统一**：用户在各域的行为共同刻画了一个关注自我形象、享受生活、追逐流行文化与剧情内容的年轻女性形象，各域兴趣相互印证，高度统一。 + +【预测总结】 +综合分析，推测用户接下来最可能感兴趣并产生点击的内容是**与短剧、网文小说相关的剧情类视频或广告，特别是那些包含“家庭财产纠纷”、“重生逆袭”、“真假千金”等强冲突元素的内容**。这一预测主要基于用户近期在广告、视频和搜索域表现出的极高互动强度和主动探索意愿，该兴趣已成为其当前最核心的关注点。 + +其次，**高品质生鲜水果(如车厘子、榴莲、蓝莓等)的促销信息、产地溯源或美食制作类视频**也可能吸引其点击。这源于她近期在商品域对该品类表现出的强烈关注和浏览行为。 + +此外，考虑到用户持续的消费习惯和季节性需求，**新款的“大码/显瘦”女装、功能性内衣，或其复购过的护肤品牌<|prod_begin|>关联的品牌)的推广内容**，也具备较高的吸引力，可能促使其发生点击或购买行为。 +该用户最近感兴趣的广告有: <|ad_begin|><|im_end|> +<|im_start|>user +你擅长理解快手用户跨场景行为和语义ID表示，请根据输入信息归纳该用户的目标内容。 +请阅读下面的用户行为记录，并推断该用户在各推荐场景中的目标内容。 + +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/首次打赏行为，首次打赏了 <|living_begin|>, <|living_begin|>, <|living_begin|>，对主播 <|living_begin|> 有过关注/打赏行为。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，看过视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|> 有过关注/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为。 +广告行为: 对广告 <|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|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|> 完成过深度转化，点击过广告 <|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|>, <|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|>。/think<|im_end|> +<|im_start|>assistant +好的，请看我的分析： + +【兴趣归纳】 +根据用户的多域行为，可以归纳出以下几个核心兴趣： +1. **游戏娱乐**：此为最核心且交互深度最广的兴趣。用户在商品域多次购买云游戏服务(...、...)及大量游戏IP衍生玩具(如光剑、模型枪、头盔等)；在广告域对多种游戏(射击、沙盒、解谜、角色扮演类)有深度转化行为；在视频和直播域也大量消费游戏内容(如《我的世界》、《和平精英》等)，并为游戏主播打赏(...、...)，形成了从认知、消费到娱乐的完整闭环。 +2. **儿童消费**：用户有明显的为孩子消费的倾向。商品购买记录中包含大量儿童玩具、早教套装(...)、儿童服饰(...)等。这一兴趣主要体现在高价值的实物购买上，是用户消费的重要组成部分。 +3. **娱乐直播**：用户对直播内容有强烈偏好，表现出高频的关注和付费行为。其兴趣主要分为两类：一类是高颜值的才艺型女主播，用户关注并打赏了大量该类型主播(...、...)；另一类是创意或手艺人主播，如签名设计、手写等，同样有付费记录(...、...)。 +4. **趣味搞笑与食品**：用户通过搜索(如搜索词“怎么用手放屁”)、观看搞笑视频(...)表现出对轻松、幽默内容的喜爱。同时，在商品域频繁购买各类糖果、零食和速食产品，表明食品是其持续的刚性需求。 + +【行为模式】 +用户的行为模式呈现出“角色化消费”和“兴趣协同”的特点。消费偏好上，用户展现了“家长”和“年轻玩家”的双重角色，既会为孩子的教育和娱乐投入，也会为自己的游戏和直播娱乐付费，且付费意愿较高，对感兴趣的商品(如糖果、速食)会进行多次复购。 + +跨域协同信号非常明显，特别是以“游戏”为核心的协同效应。例如，用户在广告域被《反恐精英》、《绝地求生》等射击游戏吸引，随后在商品域购买了相关的模型玩具枪(...)；在购买了沙盒模拟器游戏后，又在视频域观看了大量《我的世界》等沙盒游戏内容。这种“内容种草-实物购买-衍生娱乐”的跨域行为链条清晰可见，表明其兴趣在不同领域间能有效传导和放大。 + +【预测总结】 +综合分析，用户接下来最可能被**融合了“游戏”与“搞笑/沙雕”元素的短视频或直播内容**所吸引。 + +推断依据如下：用户的核心兴趣“游戏”和“趣味搞笑”具有高度的重合性与协同潜力。历史行为显示，用户购买了多款沙盒模拟器类游戏(...)，并对“沙雕”、“搞笑”标签的视频和直播有高互动。因此，一个标题或封面带有“游戏BUG集锦”、“沙雕玩家欢乐实况”或类似元素的《我的世界》、《和平精英》等热门游戏视频，有较大几率会触发其点击。此外，考虑到用户对新奇玩具的持续关注，与游戏IP联名的、具有新奇玩法或搞怪属性的玩具商品广告，也可能会吸引其注意。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|im_start|>user +你要把用户历史行为转换成推荐目标描述，请保持输出简洁、准确，并覆盖所有非空场景。 +已知如下用户跨场景行为： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|> 有过转发/收藏/点赞/长播行为，对视频 <|video_begin|> 有过关注/收藏/长播行为，对视频 <|video_begin|> 有过转发/点赞/长播行为，对视频 <|video_begin|> 有过关注/转发/收藏/点赞/长播行为，点赞过 <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，看过视频 <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/评论/收藏/点赞/长播行为，评论过 <|video_begin|>，对视频 <|video_begin|> 有过评论/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|> 有过关注/收藏/点赞/长播行为，对视频 <|video_begin|> 有过关注/评论/收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/长播行为。 +广告行为: 点击过广告 <|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|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，遵照您的指示，我将作为推荐系统分析助手，对该用户的行为历史进行分析和预测。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出其三大核心兴趣： +1. **汽车资讯**：此为用户当前最强烈的兴趣。该兴趣表现出极高的交互深度和意图，尤其在近期(2026-01-25之后)集中爆发。用户进行了多次高频搜索(如 `search_id=xxx` ×15)，并对汽车对比、购车指南、车型评测等视频内容有大量关注、收藏和长播行为(如 `<|video_begin|>`, `<|video_begin|>`)，甚至关注了相关领域的直播主(`<|living_begin|>`)，显示其可能处于购车决策阶段。 +2. **时事社会与知识**：这是用户长期稳定的泛兴趣。用户广泛涉猎国际局势、国内政策、社会事件、历史人文等内容，并有深度的交互行为，如对多个相关视频进行了评论、收藏和点赞(如 `<|video_begin|>`, `<|video_begin|>`)。这表明用户有通过内容消费获取信息、洞察社会的习惯。 +3. **生活实用与健康**：该兴趣贯穿用户的消费和内容浏览。电商行为中，用户购买了大量男士与儿童服饰、家居用品及多种健康养生类商品(如 `<|prod_begin|>`)。同时，用户也关注健康科普、生活技巧、法律常识等实用性视频(如 `<|video_begin|>`, `<|video_begin|>`)，体现了其作为家庭支柱，对生活品质和家人健康的关切。 + +【行为模式】 +用户的消费偏好呈现出典型的**务实、注重性价比**的特点，购买商品多为满足家庭刚需的服饰、日用及健康产品，且对促销广告(如 `<|ad_begin|>`)有较高的点击偏好。其行为模式表现出显著的**跨域协同信号**：首先，**搜索行为直接驱动内容消费**，例如在多次搜索特定汽车信息后，用户会深度消费相关的评测视频和直播。其次，**内容兴趣与消费行为高度一致**，对健康养生视频的关注(`<|video_begin|>`)与购买多种滋补养生类商品的行为(`<|prod_begin|>`)相互印证，形成了完整的兴趣闭环。此外，用户对美女、才艺类直播的早期关注，也体现了其在娱乐放松方面的需求。 + +【预测总结】 +综合分析，用户接下来最可能感兴趣的内容类型是**汽车相关资讯，特别是针对具体车型的深度对比、价格分析以及本地经销商的优惠促销信息**。推断依据是用户近期在该领域表现出的极高频次的搜索行为和深度视频互动，这表明其信息需求强烈且处于决策关键期。因此，关于本田、日产、大众等品牌SUV车型的性价比分析、车主真实体验分享或秦皇岛地区的4S店直播、探店视频，有较大几率吸引其点击。此外，鉴于其稳定的时事兴趣，关于社会热点、经济政策的深度解读类视频也可能获得用户的持续关注。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|im_start|>user +你是一位推荐系统分析助手，请阅读用户历史行为，并给出直播、电商、视频、广告场景的目标内容。 +已知如下用户跨场景行为： +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|> 有过转发/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/转发/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/转发/收藏/长播行为，观看 <|video_begin|> 时触发过关注，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/评论/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/评论/收藏/点赞/长播行为，看过视频 <|video_begin|>, <|video_begin|>，对视频 <|video_begin|> 有过关注/评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|> 有过关注/评论/长播行为，对视频 <|video_begin|> 有过关注/转发/评论/收藏/点赞/长播行为，对视频 <|video_begin|> 有过转发/评论/收藏/点赞/长播行为。 +广告行为: 对广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|> 完成过深度转化，点击过广告 <|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|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，作为推荐系统分析助手，我将根据您提供的用户多域历史行为进行分析。 + +【兴趣归纳】： +用户的核心兴趣主要集中在以下三个类目： +1. **休闲游戏与电竞**：此为用户最核心且交互最深的兴趣。用户对多款卡通风格的派对游戏如<|video_begin|>和像素方块游戏如<|video_begin|>表现出极高的粘性，存在大量关注、收藏、转发及长播等深度互动行为。此外，对其他热门手游如竞速、MOBA类也有涉猎。 +2. **亲子教育与儿童内容**：该兴趣贯穿视频、搜索、广告和电商等多个领域。用户频繁搜索并观看萌娃、儿童搞笑、卡通动画如<|video_begin|>等内容，并有多次收藏和点赞。同时，用户点击了多款数学、英语等教育类游戏广告，并在电商域点击了大量小学阶段的教辅教材及文具商品如<|prod_begin|>，表明其强烈的家长角色属性和对子女教育的关注。 +3. **动漫与二次元文化**：用户对国产经典动画IP如<|video_begin|>及日式校园模拟游戏如<|video_begin|>均有高频互动。内容消费偏好轻松、幽默的“沙雕动画”和剧情向的二次元内容，交互行为以关注和收藏为主。 + +【行为模式】： +用户的消费偏好清晰地指向**家庭和亲子场景**。其在电商域对小学教材、教辅及书皮等学习用品的集中点击行为，是解读其跨域行为的关键信号。这一行为模式解释了其在视频域对儿童动画、萌娃日常、教育类内容的强烈偏好，表明其内容消费很可能受到家庭中低龄子女的影响或直接为其服务。用户的跨域协同信号非常明显：例如，搜索“萌娃搞笑日常”后，会持续消费相关主题的短视频；点击教育类游戏广告后，会进一步在电商域浏览相关教辅材料。用户是高价值的互动用户，对感兴趣的内容乐于进行关注、收藏、转发等深度操作。 + +【预测总结】： +基于以上分析，用户接下来可能对以下几类内容表现出较高的兴趣： +1. **热门休闲游戏的新攻略或趣味集锦**：特别是用户已深度互动的几款卡通派对游戏和像素方块游戏，相关的新赛季内容、高阶玩法技巧或搞笑失误集锦可能会吸引其再次深度点击和互动。 +2. **亲子互动或家庭教育类短视频**：例如儿童安全知识科普、趣味科学小实验、家庭情景喜剧等，这类内容完美契合其“家长”身份和已表现出的教育关切。 +3. **知名儿童动画IP的衍生内容**：鉴于用户对经典动画的持续关注，围绕这些IP创作的二创短剧、剧情解说或剧场版预告等，有较大几率获得用户的青睐。 +该用户最近点击了商品: <|prod_begin|><|im_end|> +<|im_start|>user +你擅长理解快手用户跨场景行为和语义ID表示，请根据输入信息归纳该用户的目标内容。 +已知如下用户跨场景行为： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，给主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 打赏过，首次打赏了 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/打赏行为，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/首次打赏行为。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，看过视频 <|video_begin|>，对视频 <|video_begin|> 有过关注/转发/收藏/点赞/长播行为。 +广告行为: 对广告 <|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|> 完成过深度转化，点击过广告 <|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|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，收到您的指令。作为推荐系统分析助手，我将根据提供的用户交互历史进行分析和预测。 + +### 用户兴趣偏好与内容推荐分析 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： + +1. **情感关系与心理探索**：这是用户近期最核心且交互最深的兴趣。用户在视频域投入了大量时间，对关于情感挽回、分手复合、两性心理分析及处理社交拉黑等主题的内容进行了高频次的点赞、评论、长播乃至转发行为如<|video_begin|>、<|video_begin|>。同时，用户对塔罗、姓名配对等带有神秘主义色彩的情感测试广告也有多次点击和深度转化，显示出其在该领域有强烈的探索与解决问题的诉求。 + +2. **珠宝文玩**：这是一个具有高商业价值的稳定兴趣。用户在直播域多次关注、打赏翡翠主播如<|living_begin|>，并在电商域有大量翡翠相关商品的购买记录如<|prod_begin|>，形成了“观看-信任-购买”的完整闭环。这表明用户不仅是欣赏，更是具备专业认知和高消费意愿的玩家。 + +3. **中老年健康养生**：这是一个持续性的刚需兴趣。用户在电商域多次购买针对耳鸣、补肾等特定需求的健康产品如<|prod_begin|>、<|prod_begin|>，并对相关主题的广告和视频内容如<|ad_begin|>表现出关注。此兴趣贯穿了较长的时间线，显示出用户正积极寻求健康问题的解决方案。 + +4. **泛文娱内容消费**：此为用户的娱乐消遣兴趣，主要体现在直播域。用户偏好与主播有亲切互动的才艺直播，尤其青睐演唱经典、怀旧、民族及情感类歌曲的女主播如<|living_begin|>，并愿意为此打赏。此外，对历史人文、军旅文化、时事解析等知识型内容也有明确的付费和关注行为。 + +【行为模式】 +用户的消费偏好呈现出“高价值信任型”和“问题解决型”两大特征。在珠宝文玩领域，用户倾向于通过关注和打赏与主播建立信任，进而进行高客单价的购买。在健康养生领域，用户的购买行为则由明确的个人健康需求驱动。 + +跨域协同信号非常显著： +- **内容种草到电商拔草**：直播域的翡翠、助农产品内容直接导向了电商域的购买行为。 +- **需求驱动的全域探索**：对“耳鸣”的健康需求，驱动了用户在电商(购买理疗产品)、广告(点击医疗广告)和短视频(观看养生知识)的系列行为。 +- **情感共鸣驱动深度互动**：在情感关系领域的困惑，使用户在短视频和广告域产生了极高的互动粘性，表现为从被动浏览到主动评论、转发的深度参与。 + +【预测总结】 +综合分析，用户当前正处于对**情感关系问题**的高度关注期，其行为表现出强烈的信息渴求和情感寄托需求。 + +因此，预测用户接下来最可能感兴趣并**点击**的内容是**关于“情感挽回”、“两性心理分析”或“如何处理分手后社交关系”的短视频或文章**。 + +推断依据如下： +1. **行为的近期性和集中性**：在最近的交互记录中，此类内容的互动密度和深度(点赞、评论、长播、转发)远超其他类目，表明这是用户当前最迫切的兴趣点。 +2. **行为的情感投入度**：用户不仅观看，还进行评论和转发，这通常意味着内容触及其痛点，引发了强烈的情感共鸣和表达欲。 +3. **跨域行为的一致性**：用户对情感测试类广告的多次深度转化，进一步印证了其希望从外部寻求情感问题答案的心理状态。 + +所以，系统若推荐情感导师讲解、过来人经验分享或心理分析类的视频，可能会获得较高的点击率。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|im_start|>user +你擅长理解快手用户跨场景行为和语义ID表示，请根据输入信息归纳该用户的目标内容。 +已知如下用户跨场景行为： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，给主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 打赏过，首次打赏了 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/打赏行为，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/首次打赏行为。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|> 有过关注/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，看过视频 <|video_begin|>，对视频 <|video_begin|> 有过关注/转发/收藏/点赞/长播行为。 +广告行为: 对广告 <|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|> 完成过深度转化，点击过广告 <|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|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，收到您的指令。作为推荐系统分析助手，我将根据提供的用户交互历史进行分析和预测。 + +### 用户兴趣偏好与内容推荐分析 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： + +1. **情感关系与心理探索**：这是用户近期最核心且交互最深的兴趣。用户在视频域投入了大量时间，对关于情感挽回、分手复合、两性心理分析及处理社交拉黑等主题的内容进行了高频次的点赞、评论、长播乃至转发行为如<|video_begin|>、<|video_begin|>。同时，用户对塔罗、姓名配对等带有神秘主义色彩的情感测试广告也有多次点击和深度转化，显示出其在该领域有强烈的探索与解决问题的诉求。 + +2. **珠宝文玩**：这是一个具有高商业价值的稳定兴趣。用户在直播域多次关注、打赏翡翠主播如<|living_begin|>，并在电商域有大量翡翠相关商品的购买记录如<|prod_begin|>，形成了“观看-信任-购买”的完整闭环。这表明用户不仅是欣赏，更是具备专业认知和高消费意愿的玩家。 + +3. **中老年健康养生**：这是一个持续性的刚需兴趣。用户在电商域多次购买针对耳鸣、补肾等特定需求的健康产品如<|prod_begin|>、<|prod_begin|>，并对相关主题的广告和视频内容如<|ad_begin|>表现出关注。此兴趣贯穿了较长的时间线，显示出用户正积极寻求健康问题的解决方案。 + +4. **泛文娱内容消费**：此为用户的娱乐消遣兴趣，主要体现在直播域。用户偏好与主播有亲切互动的才艺直播，尤其青睐演唱经典、怀旧、民族及情感类歌曲的女主播如<|living_begin|>，并愿意为此打赏。此外，对历史人文、军旅文化、时事解析等知识型内容也有明确的付费和关注行为。 + +【行为模式】 +用户的消费偏好呈现出“高价值信任型”和“问题解决型”两大特征。在珠宝文玩领域，用户倾向于通过关注和打赏与主播建立信任，进而进行高客单价的购买。在健康养生领域，用户的购买行为则由明确的个人健康需求驱动。 + +跨域协同信号非常显著： +- **内容种草到电商拔草**：直播域的翡翠、助农产品内容直接导向了电商域的购买行为。 +- **需求驱动的全域探索**：对“耳鸣”的健康需求，驱动了用户在电商(购买理疗产品)、广告(点击医疗广告)和短视频(观看养生知识)的系列行为。 +- **情感共鸣驱动深度互动**：在情感关系领域的困惑，使用户在短视频和广告域产生了极高的互动粘性，表现为从被动浏览到主动评论、转发的深度参与。 + +【预测总结】 +综合分析，用户当前正处于对**情感关系问题**的高度关注期，其行为表现出强烈的信息渴求和情感寄托需求。 + +因此，预测用户接下来最可能感兴趣并**点击**的内容是**关于“情感挽回”、“两性心理分析”或“如何处理分手后社交关系”的短视频或文章**。 + +推断依据如下： +1. **行为的近期性和集中性**：在最近的交互记录中，此类内容的互动密度和深度(点赞、评论、长播、转发)远超其他类目，表明这是用户当前最迫切的兴趣点。 +2. **行为的情感投入度**：用户不仅观看，还进行评论和转发，这通常意味着内容触及其痛点，引发了强烈的情感共鸣和表达欲。 +3. **跨域行为的一致性**：用户对情感测试类广告的多次深度转化，进一步印证了其希望从外部寻求情感问题答案的心理状态。 + +所以，系统若推荐情感导师讲解、过来人经验分享或心理分析类的视频，可能会获得较高的点击率。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|im_start|>user +你是一位推荐系统分析助手，请阅读用户历史行为，并给出直播、电商、视频、广告场景的目标内容。 +已知如下用户跨场景行为： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过关注/点赞/长播行为，对视频 <|video_begin|> 有过转发/评论/点赞/长播行为，对视频 <|video_begin|> 有过收藏/点赞/长播行为，对视频 <|video_begin|> 有过关注/评论/收藏/点赞/长播行为。 +广告行为: 对广告 <|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|>, <|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|> 完成过深度转化，点击过广告 <|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|>, <|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|>。 + +请输出该用户在不同场景下对应的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，基于您提供的用户交互历史，我将进行分析并提供我的推荐洞察。 + +【兴趣归纳】 +根据用户的多域行为，可以归纳出三大核心兴趣： +1. **动手实践与农耕生活**：这是用户最核心且投入最深的兴趣。用户在电商领域有大量且高频的购买行为(<|prod_begin|>、<|prod_begin|> 等)，集中于各类五金工具、电动设备、农用物资及种子。同时，在视频域，用户也多次关注、点赞、长播了关于农活技艺、工具制造与使用、动物养护(如修蹄)等内容(<|video_begin|>、 等)，形成了从工具购买到内容消费的完整闭环。 +2. **剧情类内容消费**：用户对短剧、动画等形式的剧情内容有广泛兴趣。其行为主要体现在广告域的深度转化(<|ad_begin|>、<|ad_begin|> 等)，内容题材偏好穿越、重生、乡土逆袭、家庭情感纠葛等。此外，用户的主动搜索行为(如搜索特定剧集)和对动画内容的浏览也印证了这一点。 +3. **接地气的地域与社会内容**：用户对具有浓厚生活气息和地域特色的内容表现出明显偏好。例如，在直播域关注了多位东北、内蒙等地的“接地气”主播(<|living_begin|>、<|living_begin|>)，内容涉及二手车、建材等实用领域。在视频域，用户也积极与社会百态、民生纪实类内容互动(<|video_begin|>、<|video_begin|>)。 + +【行为模式】 +用户的消费偏好呈现出典型的“实用主义”和“自给自足”的特点，消费决策高度集中于能够提升生产效率和解决实际问题的工具、设备及农用物资，而非时尚或享乐型商品。 +跨域协同信号非常强烈且一致： +- **“农与工”主题的协同**：用户在直播域关注二手车、建材主播，在电商域购买大量工农具，同时在视频域观看农耕、机械维修、手工制造等内容，形成了一条清晰的“看-买-学”行为链路。 +- **“剧情与现实”的融合**：用户消费的短剧内容(如重生回乡搞事业)与其现实中的农耕、动手兴趣高度相关，表明其在娱乐消费中也在寻求与自身生活经验或向往的共鸣。 +- **“地域与人物”的偏好**：用户对东北等北方地域文化和“接地气”、“实在”人设的偏好，贯穿了直播、视频和广告等多个消费场景。 + +【预测总结】 +综合分析，推测用户接下来最可能对以下两类内容产生高兴趣： +1. **硬核DIY与建造纪实类视频**：基于用户庞大的工具库和对动手实践的热衷，推荐如“用废旧材料自建温室”、“老式发动机翻新教程”或“传统木工/铁艺工艺揭秘”等内容，可能会获得用户的深度互动(如收藏、评论)。这类内容完美契合了用户的工具购买行为和求知、实践的内在动机。 +2. **融合了“基建/种田”元素的乡土短剧**：考虑到用户对剧情内容和农耕生活的双重兴趣，一部主角利用现代知识或特殊技能，在乡土环境中进行种植、养殖、建造或工业生产的短剧，很可能精准命中用户的“爽点”。这类内容将用户的现实兴趣与戏剧化叙事相结合，有较大几率促成其点击和追看。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|im_start|>user +你要把用户历史行为转换成推荐目标描述，请保持输出简洁、准确，并覆盖所有非空场景。 +下面给出用户历史行为线索： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，对主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 有过关注/首次打赏行为，首次打赏了 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，给主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 打赏过，对主播 <|living_begin|>, <|living_begin|> 有过关注/打赏行为。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，加购了 <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|> 有过评论/收藏/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|> 有过转发/评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，对视频 <|video_begin|> 有过关注/长播行为，看过视频 <|video_begin|>, <|video_begin|>，对视频 <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|> 有过关注/评论/长播行为。 +广告行为: 对广告 <|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|> 完成过深度转化，点击过广告 <|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|>, <|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|>。 + +请基于这些线索总结该用户在各场景中的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，收到您的指令。作为推荐系统分析助手，我将根据该用户的多域历史行为进行分析，并预测其潜在兴趣。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出三大核心兴趣类目： +1. **游戏内容(深度核心)**：这是用户最突出且投入最深的兴趣。行为贯穿直播、广告、视频、电商和搜索全域。在直播域，用户频繁关注并打赏游戏主播(<|living_begin|>、<|living_begin|>)，覆盖射击(如和平精英)、MOBA(如王者荣耀)、沙盒(如我的世界)和策略(如红色警戒)等多种类型，偏好技术流、教学向和高互动娱乐型主播。在电商域，用户多次购买游戏装备、周边及虚拟道具(<|prod_begin|>、<|prod_begin|>)。同时，用户对游戏广告有极高的点击和转化率(<|ad_begin|>、<|ad_begin|>)，显示出强烈的探索欲。 +2. **高颜值/陪伴型内容(高频兴趣)**：用户对高颜值主播，特别是女性主播，表现出持续的关注和付费意愿。其偏好标签包括“清纯气质”、“身材比例佳”、“甜美系”、“性感曲线”等(<|living_begin|>、<|living_begin|>)。除了外形，用户同样重视“互动陪伴”、“情感共鸣”和“真诚”等特质。该兴趣也体现在电商消费中，如购买美女主题的动态壁纸<|prod_begin|>和虚拟陪伴类游戏<|prod_begin|>。 +3. **娱乐搞笑/解压内容(稳定兴趣)**：用户对轻松、幽默、接地气的内容有稳定偏好。这体现在关注“搞笑互动”、“无厘头”、“整活”型主播(<|living_begin|>、<|living_begin|>)，观看搞笑视频，以及购买解压玩具<|prod_begin|>和零食<|prod_begin|>等行为上。 + +【行为模式】 +用户的消费偏好呈现出“为热爱付费”和“为情绪买单”的双重特征。他愿意为游戏优势(购买装备)、精神愉悦(打赏主播)和即时满足(购买零食、虚拟商品)进行消费。其付费行为集中在直播打赏和电商购物，尤其在游戏相关虚拟商品上表现出高复购率。 +跨域协同信号非常明显：用户的兴趣在不同场景下相互印证和加强。例如，观看游戏直播(直播域)会驱动其搜索游戏攻略(搜索域)、购买游戏道具(电商域)，并点击相关新游戏广告(广告域)。同样，对高颜值内容的偏好也从直播域延伸至视频消费和虚拟商品购买，形成了一个完整的“兴趣-探索-消费”闭环。 + +【预测总结】 +综合分析，用户接下来最可能感兴趣的内容大概率仍会聚焦于其核心兴趣领域。 +1. **游戏直播或视频**：特别是关于《和平精英》或《王者荣耀》的内容。一个“不露脸”但技术高超或解说风趣的教学型主播<|living_begin|>，或是一个展示高能操作、戏剧性翻盘的短视频<|video_begin|>，可能会吸引其点击。 +2. **高颜值互动内容**：一位兼具甜美外形与亲和互动特质的女性主播，无论是进行游戏直播还是才艺陪伴，都有较大几率获得用户的关注和互动。带有“二次元”、“纯欲风”、“JK”等特定视觉风格的虚拟主播或真人主播<|living_begin|>也可能引发其兴趣。 +推断依据是用户在这些领域的行为密度、互动深度(含付费)和跨域一致性最高，且近期行为(近三个月)依然高度集中于此，表明这些是其稳定且活跃的兴趣点。 +该用户最近首次打赏了主播: <|living_begin|><|im_end|> +<|im_start|>user +你是一名多场景推荐数据构造助手，请结合用户行为线索，生成对应的目标内容文本。 +下面给出用户历史行为线索： +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过转发/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过收藏/长播行为，看过视频 <|video_begin|>，对视频 <|video_begin|> 有过转发/收藏/长播行为。 +广告行为: 对广告 <|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|> 完成过深度转化，点击过广告 <|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|>。 + +请基于这些线索总结该用户在各场景中的目标内容。/think<|im_end|> +<|im_start|>assistant +好的，作为您的推荐系统分析助手，我已根据用户的多域历史行为进行了分析。以下是我的报告： + +### 【兴趣归纳】 +根据用户的交互历史，可以归纳出以下几个核心兴趣类目，其行为深度各不相同： + +1. **家庭生活与子女教育 (深度)**：这是用户最核心且投入最深的兴趣领域。行为贯穿商品、搜索和视频域。在**商品域**，用户有大量直接购买行为，覆盖了从K-12阶段的数学、语文、英语等教辅材料<|prod_begin|>, <|prod_begin|>)到儿童保健品<|prod_begin|>及儿童服饰与玩具。在**搜索域**，用户多次搜索与低年级教育相关的资源(如搜索“一年级题每日一练可打印”)。在**视频/广告域**，用户点击了儿童阅读平台<|ad_begin|>和育儿起名相关的广告，并关注家庭关系、教育理念的视频内容。 + +2. **短剧及影视娱乐 (深度)**：用户对短剧内容表现出极高的兴趣和粘性。在**广告域**，用户有超过20次针对各类短剧的点击和深度转化行为，题材高度集中于古装、奇幻、逆袭、言情等<|ad_begin|>, <|ad_begin|>、<|ad_begin|>)。这种高频的“深度转化”表明用户不仅被预告片吸引，还进行了观看、追剧等后续深入行为。在**视频域**，用户也主动消费与历史、情感、名人轶事相关的内容，这些是短剧的常见衍生或关联话题。 + +3. **实用生活与健康养生 (中到深度)**：该兴趣体现在解决实际生活问题和对健康的关注上。在**商品域**，用户购买了大量家居实用物品，如厨具<|prod_begin|>、收纳用品<|prod_begin|>、汽车配件<|prod_begin|>和家电维修零件<|prod_begin|>。同时，用户对养生保健有强烈需求，购买了多种传统滋补品、养生茶饮和保健书籍(<|prod_begin|>、<|prod_begin|>)。在**视频域**，用户收藏了多个烹饪教程<|video_begin|>和家居维修<|video_begin|>的视频，展现出强烈的学习和实践意愿。 + +### 【行为模式】 +用户的行为模式呈现出鲜明的**“家庭为中心”的务实型消费观**与**“娱乐驱动”的信息消费观**。 + +消费偏好上，用户是典型的家庭决策者，购买行为以满足家庭成员(尤其是孩子和长辈)的需求以及提升生活便利性为导向。其选购商品时注重实用性和性价比，例如购买厨房收纳、维修配件、多包装食品等。同时，用户有明确的“问题解决”导向，会通过搜索了解信息(如搜索汽车功能、家电信息)，然后进行相关消费(如购买汽车配件、浴霸)。 + +跨域协同信号非常显著： +* **视频/搜索驱动消费**：用户在视频域收藏烹饪、维修等“How-to”内容，并在商品域购买相关工具和食材，形成了从“学”到“用”的闭环。 +* **兴趣一致性**：用户对中医养生的兴趣同时体现在购买滋补品、养生书籍和浏览相关视频上。对子女教育的焦虑也同时反映在购买教辅、搜索学习资料和点击教育类广告上。 +* **娱乐内容消费路径**：用户通过广告被引入短剧，形成观看习惯后，会进一步消费相关的历史、情感类视频内容，展现了从被动接受到主动探索的兴趣深化路径。 + +### 【预测总结】 +综合分析，用户接下来最可能感兴趣并产生点击行为的内容，大概率是**短视频**形态，具体可细分为以下几类： + +1. **古装/家庭情感类短剧**：这是用户近期互动频率最高、付费转化意愿最强的类目。基于其对古装、逆袭、家庭纠葛等主题的持续“深度转化”行为<|ad_begin|>, <|ad_begin|>)，系统推荐具有相似元素的短剧切片或预告，有极大概率获得点击。 + +2. **生活窍门或烹饪教学类视频**：用户有多次收藏(<|video_begin|>、<|video_begin|>)此类视频的行为，表明其认为这类内容具有长期价值。因此，推送关于“家常菜新做法”、“旧物改造”、“家居收纳技巧”等实用性强的内容，很可能引发用户的收藏和观看行为。 + +3. **教育理念或社会热点解读类视频**：鉴于用户对子女教育的深度投入和对社会新闻的关注<|video_begin|>, <|video_begin|>)，推送有关“如何辅导孩子作业”、“解读最新教育政策”或近期社会热点事件的深度评论，可能会满足其信息获取和观点参考的需求，从而产生转发或评论互动。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|endoftext|><|endoftext|> +label_ids: +[-100, -100, 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+用户的消费偏好呈现出务实与兴趣导向并存的特点。一方面，她会购买“二级品”、“瑕疵款”商品，并关注“省钱技巧”类内容，显示出对高性价比的追求。另一方面，她也会被即时兴趣驱动，大量购买和点击各类零食、水果。其购物行为存在明显的复购周期，如对某品牌面霜<|prod_begin|>和洗发水<|prod_begin|>的多次购买。 + +用户的跨域协同信号非常强烈： +* **内容引导消费(C2E)**：用户在观看羽绒服穿搭<|video_begin|>、车厘子介绍<|video_begin|>等视频后，在商品域产生了大量相关品类的点击行为，展现了从内容种草到消费意向的直接转化路径。 +* **搜索驱动内容消费**：用户在搜索域探索特定小说剧情后，其信息流中显著增加了同类型题材的短剧广告和视频推荐，并产生了大量深度转化，表明搜索行为是其内容消费的强力风向标。 +* **兴趣身份统一**：用户在各域的行为共同刻画了一个关注自我形象、享受生活、追逐流行文化与剧情内容的年轻女性形象，各域兴趣相互印证，高度统一。 + +【预测总结】 +综合分析，推测用户接下来最可能感兴趣并产生点击的内容是**与短剧、网文小说相关的剧情类视频或广告，特别是那些包含“家庭财产纠纷”、“重生逆袭”、“真假千金”等强冲突元素的内容**。这一预测主要基于用户近期在广告、视频和搜索域表现出的极高互动强度和主动探索意愿，该兴趣已成为其当前最核心的关注点。 + +其次，**高品质生鲜水果(如车厘子、榴莲、蓝莓等)的促销信息、产地溯源或美食制作类视频**也可能吸引其点击。这源于她近期在商品域对该品类表现出的强烈关注和浏览行为。 + +此外，考虑到用户持续的消费习惯和季节性需求，**新款的“大码/显瘦”女装、功能性内衣，或其复购过的护肤品牌<|prod_begin|>关联的品牌)的推广内容**，也具备较高的吸引力，可能促使其发生点击或购买行为。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，遵照您的指示，以下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】 +根据用户的多域交互历史，可以归纳出其核心兴趣主要集中在以下四个方面： +1. **剧情内容与情感故事**：这是用户近期最核心且互动最深的兴趣。自2025年12月起，用户对以短剧、网文小说为代表的剧情内容表现出极高热情。其行为包括对大量此类广告的深度转化<|ad_begin|>, <|ad_begin|>等)，以及在搜索域进行高频、重复的主动探索(如对特定剧情设定的7次和5次搜索)。内容主题多为家庭矛盾、情感纠葛、职场逆袭、古风宫斗等强戏剧性元素。 +2. **女性时尚与个护美妆**：此为用户长期且稳定的消费兴趣。用户在商品域有多次购买(`商品-购买`)和大量点击(`商品-点击`)行为，覆盖了从服饰(特别是强调“大码”、“显瘦”、“塑形”功能的款式)、内衣、鞋包到美妆护肤(面霜、精华、洗发水、唇膏等)及个护(女性清洗液、卫生巾)的全链路。近期对羽绒服、大衣等冬季服饰有密集的点击行为。 +3. **居家生活与美食**：用户对此类目表现出高频的消费和浏览行为。购买记录(`商品-购买`)横跨休闲零食、代餐麦片、蛋糕烘焙、生鲜水果等多个品类，显示出对即食和健康食品的偏好。近期对车厘子、蓝莓等高客单价水果有大量点击，并对烹饪、手工DIY、收纳整理等视频内容有收藏、点赞等深度互动<|video_begin|>。 +4. **娱乐八卦与社会热点**：这是一个泛兴趣领域，用户通过主动搜索和视频点赞表现出关注。内容涉及明星艺人、网红动态、影视剧集(如《甄嬛传》)的二次创作以及社会事件等。互动深度中等，主要为浏览、点赞和搜索行为。 + +【行为模式】 +用户的消费偏好呈现出务实与兴趣导向并存的特点。一方面，她会购买“二级品”、“瑕疵款”商品，并关注“省钱技巧”类内容，显示出对高性价比的追求。另一方面，她也会被即时兴趣驱动，大量购买和点击各类零食、水果。其购物行为存在明显的复购周期，如对某品牌面霜<|prod_begin|>和洗发水<|prod_begin|>的多次购买。 + +用户的跨域协同信号非常强烈： +* **内容引导消费(C2E)**：用户在观看羽绒服穿搭<|video_begin|>、车厘子介绍<|video_begin|>等视频后，在商品域产生了大量相关品类的点击行为，展现了从内容种草到消费意向的直接转化路径。 +* **搜索驱动内容消费**：用户在搜索域探索特定小说剧情后，其信息流中显著增加了同类型题材的短剧广告和视频推荐，并产生了大量深度转化，表明搜索行为是其内容消费的强力风向标。 +* **兴趣身份统一**：用户在各域的行为共同刻画了一个关注自我形象、享受生活、追逐流行文化与剧情内容的年轻女性形象，各域兴趣相互印证，高度统一。 + +【预测总结】 +综合分析，推测用户接下来最可能感兴趣并产生点击的内容是**与短剧、网文小说相关的剧情类视频或广告，特别是那些包含“家庭财产纠纷”、“重生逆袭”、“真假千金”等强冲突元素的内容**。这一预测主要基于用户近期在广告、视频和搜索域表现出的极高互动强度和主动探索意愿，该兴趣已成为其当前最核心的关注点。 + +其次，**高品质生鲜水果(如车厘子、榴莲、蓝莓等)的促销信息、产地溯源或美食制作类视频**也可能吸引其点击。这源于她近期在商品域对该品类表现出的强烈关注和浏览行为。 + +此外，考虑到用户持续的消费习惯和季节性需求，**新款的“大码/显瘦”女装、功能性内衣，或其复购过的护肤品牌<|prod_begin|>关联的品牌)的推广内容**，也具备较高的吸引力，可能促使其发生点击或购买行为。 +该用户最近感兴趣的广告有: <|ad_begin|><|im_end|> +好的，请看我的分析： + +【兴趣归纳】 +根据用户的多域行为，可以归纳出以下几个核心兴趣： +1. **游戏娱乐**：此为最核心且交互深度最广的兴趣。用户在商品域多次购买云游戏服务(...、...)及大量游戏IP衍生玩具(如光剑、模型枪、头盔等)；在广告域对多种游戏(射击、沙盒、解谜、角色扮演类)有深度转化行为；在视频和直播域也大量消费游戏内容(如《我的世界》、《和平精英》等)，并为游戏主播打赏(...、...)，形成了从认知、消费到娱乐的完整闭环。 +2. **儿童消费**：用户有明显的为孩子消费的倾向。商品购买记录中包含大量儿童玩具、早教套装(...)、儿童服饰(...)等。这一兴趣主要体现在高价值的实物购买上，是用户消费的重要组成部分。 +3. **娱乐直播**：用户对直播内容有强烈偏好，表现出高频的关注和付费行为。其兴趣主要分为两类：一类是高颜值的才艺型女主播，用户关注并打赏了大量该类型主播(...、...)；另一类是创意或手艺人主播，如签名设计、手写等，同样有付费记录(...、...)。 +4. **趣味搞笑与食品**：用户通过搜索(如搜索词“怎么用手放屁”)、观看搞笑视频(...)表现出对轻松、幽默内容的喜爱。同时，在商品域频繁购买各类糖果、零食和速食产品，表明食品是其持续的刚性需求。 + +【行为模式】 +用户的行为模式呈现出“角色化消费”和“兴趣协同”的特点。消费偏好上，用户展现了“家长”和“年轻玩家”的双重角色，既会为孩子的教育和娱乐投入，也会为自己的游戏和直播娱乐付费，且付费意愿较高，对感兴趣的商品(如糖果、速食)会进行多次复购。 + +跨域协同信号非常明显，特别是以“游戏”为核心的协同效应。例如，用户在广告域被《反恐精英》、《绝地求生》等射击游戏吸引，随后在商品域购买了相关的模型玩具枪(...)；在购买了沙盒模拟器游戏后，又在视频域观看了大量《我的世界》等沙盒游戏内容。这种“内容种草-实物购买-衍生娱乐”的跨域行为链条清晰可见，表明其兴趣在不同领域间能有效传导和放大。 + +【预测总结】 +综合分析，用户接下来最可能被**融合了“游戏”与“搞笑/沙雕”元素的短视频或直播内容**所吸引。 + +推断依据如下：用户的核心兴趣“游戏”和“趣味搞笑”具有高度的重合性与协同潜力。历史行为显示，用户购买了多款沙盒模拟器类游戏(...)，并对“沙雕”、“搞笑”标签的视频和直播有高互动。因此，一个标题或封面带有“游戏BUG集锦”、“沙雕玩家欢乐实况”或类似元素的《我的世界》、《和平精英》等热门游戏视频，有较大几率会触发其点击。此外，考虑到用户对新奇玩具的持续关注，与游戏IP联名的、具有新奇玩法或搞怪属性的玩具商品广告，也可能会吸引其注意。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，遵照您的指示，我将作为推荐系统分析助手，对该用户的行为历史进行分析和预测。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出其三大核心兴趣： +1. **汽车资讯**：此为用户当前最强烈的兴趣。该兴趣表现出极高的交互深度和意图，尤其在近期(2026-01-25之后)集中爆发。用户进行了多次高频搜索(如 `search_id=xxx` ×15)，并对汽车对比、购车指南、车型评测等视频内容有大量关注、收藏和长播行为(如 `<|video_begin|>`, `<|video_begin|>`)，甚至关注了相关领域的直播主(`<|living_begin|>`)，显示其可能处于购车决策阶段。 +2. **时事社会与知识**：这是用户长期稳定的泛兴趣。用户广泛涉猎国际局势、国内政策、社会事件、历史人文等内容，并有深度的交互行为，如对多个相关视频进行了评论、收藏和点赞(如 `<|video_begin|>`, `<|video_begin|>`)。这表明用户有通过内容消费获取信息、洞察社会的习惯。 +3. **生活实用与健康**：该兴趣贯穿用户的消费和内容浏览。电商行为中，用户购买了大量男士与儿童服饰、家居用品及多种健康养生类商品(如 `<|prod_begin|>`)。同时，用户也关注健康科普、生活技巧、法律常识等实用性视频(如 `<|video_begin|>`, `<|video_begin|>`)，体现了其作为家庭支柱，对生活品质和家人健康的关切。 + +【行为模式】 +用户的消费偏好呈现出典型的**务实、注重性价比**的特点，购买商品多为满足家庭刚需的服饰、日用及健康产品，且对促销广告(如 `<|ad_begin|>`)有较高的点击偏好。其行为模式表现出显著的**跨域协同信号**：首先，**搜索行为直接驱动内容消费**，例如在多次搜索特定汽车信息后，用户会深度消费相关的评测视频和直播。其次，**内容兴趣与消费行为高度一致**，对健康养生视频的关注(`<|video_begin|>`)与购买多种滋补养生类商品的行为(`<|prod_begin|>`)相互印证，形成了完整的兴趣闭环。此外，用户对美女、才艺类直播的早期关注，也体现了其在娱乐放松方面的需求。 + +【预测总结】 +综合分析，用户接下来最可能感兴趣的内容类型是**汽车相关资讯，特别是针对具体车型的深度对比、价格分析以及本地经销商的优惠促销信息**。推断依据是用户近期在该领域表现出的极高频次的搜索行为和深度视频互动，这表明其信息需求强烈且处于决策关键期。因此，关于本田、日产、大众等品牌SUV车型的性价比分析、车主真实体验分享或秦皇岛地区的4S店直播、探店视频，有较大几率吸引其点击。此外，鉴于其稳定的时事兴趣，关于社会热点、经济政策的深度解读类视频也可能获得用户的持续关注。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，作为推荐系统分析助手，我将根据您提供的用户多域历史行为进行分析。 + +【兴趣归纳】： +用户的核心兴趣主要集中在以下三个类目： +1. **休闲游戏与电竞**：此为用户最核心且交互最深的兴趣。用户对多款卡通风格的派对游戏如<|video_begin|>和像素方块游戏如<|video_begin|>表现出极高的粘性，存在大量关注、收藏、转发及长播等深度互动行为。此外，对其他热门手游如竞速、MOBA类也有涉猎。 +2. **亲子教育与儿童内容**：该兴趣贯穿视频、搜索、广告和电商等多个领域。用户频繁搜索并观看萌娃、儿童搞笑、卡通动画如<|video_begin|>等内容，并有多次收藏和点赞。同时，用户点击了多款数学、英语等教育类游戏广告，并在电商域点击了大量小学阶段的教辅教材及文具商品如<|prod_begin|>，表明其强烈的家长角色属性和对子女教育的关注。 +3. **动漫与二次元文化**：用户对国产经典动画IP如<|video_begin|>及日式校园模拟游戏如<|video_begin|>均有高频互动。内容消费偏好轻松、幽默的“沙雕动画”和剧情向的二次元内容，交互行为以关注和收藏为主。 + +【行为模式】： +用户的消费偏好清晰地指向**家庭和亲子场景**。其在电商域对小学教材、教辅及书皮等学习用品的集中点击行为，是解读其跨域行为的关键信号。这一行为模式解释了其在视频域对儿童动画、萌娃日常、教育类内容的强烈偏好，表明其内容消费很可能受到家庭中低龄子女的影响或直接为其服务。用户的跨域协同信号非常明显：例如，搜索“萌娃搞笑日常”后，会持续消费相关主题的短视频；点击教育类游戏广告后，会进一步在电商域浏览相关教辅材料。用户是高价值的互动用户，对感兴趣的内容乐于进行关注、收藏、转发等深度操作。 + +【预测总结】： +基于以上分析，用户接下来可能对以下几类内容表现出较高的兴趣： +1. **热门休闲游戏的新攻略或趣味集锦**：特别是用户已深度互动的几款卡通派对游戏和像素方块游戏，相关的新赛季内容、高阶玩法技巧或搞笑失误集锦可能会吸引其再次深度点击和互动。 +2. **亲子互动或家庭教育类短视频**：例如儿童安全知识科普、趣味科学小实验、家庭情景喜剧等，这类内容完美契合其“家长”身份和已表现出的教育关切。 +3. **知名儿童动画IP的衍生内容**：鉴于用户对经典动画的持续关注，围绕这些IP创作的二创短剧、剧情解说或剧场版预告等，有较大几率获得用户的青睐。 +该用户最近点击了商品: <|prod_begin|><|im_end|> +好的，收到您的指令。作为推荐系统分析助手，我将根据提供的用户交互历史进行分析和预测。 + +### 用户兴趣偏好与内容推荐分析 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： + +1. **情感关系与心理探索**：这是用户近期最核心且交互最深的兴趣。用户在视频域投入了大量时间，对关于情感挽回、分手复合、两性心理分析及处理社交拉黑等主题的内容进行了高频次的点赞、评论、长播乃至转发行为如<|video_begin|>、<|video_begin|>。同时，用户对塔罗、姓名配对等带有神秘主义色彩的情感测试广告也有多次点击和深度转化，显示出其在该领域有强烈的探索与解决问题的诉求。 + +2. **珠宝文玩**：这是一个具有高商业价值的稳定兴趣。用户在直播域多次关注、打赏翡翠主播如<|living_begin|>，并在电商域有大量翡翠相关商品的购买记录如<|prod_begin|>，形成了“观看-信任-购买”的完整闭环。这表明用户不仅是欣赏，更是具备专业认知和高消费意愿的玩家。 + +3. **中老年健康养生**：这是一个持续性的刚需兴趣。用户在电商域多次购买针对耳鸣、补肾等特定需求的健康产品如<|prod_begin|>、<|prod_begin|>，并对相关主题的广告和视频内容如<|ad_begin|>表现出关注。此兴趣贯穿了较长的时间线，显示出用户正积极寻求健康问题的解决方案。 + +4. **泛文娱内容消费**：此为用户的娱乐消遣兴趣，主要体现在直播域。用户偏好与主播有亲切互动的才艺直播，尤其青睐演唱经典、怀旧、民族及情感类歌曲的女主播如<|living_begin|>，并愿意为此打赏。此外，对历史人文、军旅文化、时事解析等知识型内容也有明确的付费和关注行为。 + +【行为模式】 +用户的消费偏好呈现出“高价值信任型”和“问题解决型”两大特征。在珠宝文玩领域，用户倾向于通过关注和打赏与主播建立信任，进而进行高客单价的购买。在健康养生领域，用户的购买行为则由明确的个人健康需求驱动。 + +跨域协同信号非常显著： +- **内容种草到电商拔草**：直播域的翡翠、助农产品内容直接导向了电商域的购买行为。 +- **需求驱动的全域探索**：对“耳鸣”的健康需求，驱动了用户在电商(购买理疗产品)、广告(点击医疗广告)和短视频(观看养生知识)的系列行为。 +- **情感共鸣驱动深度互动**：在情感关系领域的困惑，使用户在短视频和广告域产生了极高的互动粘性，表现为从被动浏览到主动评论、转发的深度参与。 + +【预测总结】 +综合分析，用户当前正处于对**情感关系问题**的高度关注期，其行为表现出强烈的信息渴求和情感寄托需求。 + +因此，预测用户接下来最可能感兴趣并**点击**的内容是**关于“情感挽回”、“两性心理分析”或“如何处理分手后社交关系”的短视频或文章**。 + +推断依据如下： +1. **行为的近期性和集中性**：在最近的交互记录中，此类内容的互动密度和深度(点赞、评论、长播、转发)远超其他类目，表明这是用户当前最迫切的兴趣点。 +2. **行为的情感投入度**：用户不仅观看，还进行评论和转发，这通常意味着内容触及其痛点，引发了强烈的情感共鸣和表达欲。 +3. **跨域行为的一致性**：用户对情感测试类广告的多次深度转化，进一步印证了其希望从外部寻求情感问题答案的心理状态。 + +所以，系统若推荐情感导师讲解、过来人经验分享或心理分析类的视频，可能会获得较高的点击率。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，收到您的指令。作为推荐系统分析助手，我将根据提供的用户交互历史进行分析和预测。 + +### 用户兴趣偏好与内容推荐分析 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出以下几个核心兴趣类目： + +1. **情感关系与心理探索**：这是用户近期最核心且交互最深的兴趣。用户在视频域投入了大量时间，对关于情感挽回、分手复合、两性心理分析及处理社交拉黑等主题的内容进行了高频次的点赞、评论、长播乃至转发行为如<|video_begin|>、<|video_begin|>。同时，用户对塔罗、姓名配对等带有神秘主义色彩的情感测试广告也有多次点击和深度转化，显示出其在该领域有强烈的探索与解决问题的诉求。 + +2. **珠宝文玩**：这是一个具有高商业价值的稳定兴趣。用户在直播域多次关注、打赏翡翠主播如<|living_begin|>，并在电商域有大量翡翠相关商品的购买记录如<|prod_begin|>，形成了“观看-信任-购买”的完整闭环。这表明用户不仅是欣赏，更是具备专业认知和高消费意愿的玩家。 + +3. **中老年健康养生**：这是一个持续性的刚需兴趣。用户在电商域多次购买针对耳鸣、补肾等特定需求的健康产品如<|prod_begin|>、<|prod_begin|>，并对相关主题的广告和视频内容如<|ad_begin|>表现出关注。此兴趣贯穿了较长的时间线，显示出用户正积极寻求健康问题的解决方案。 + +4. **泛文娱内容消费**：此为用户的娱乐消遣兴趣，主要体现在直播域。用户偏好与主播有亲切互动的才艺直播，尤其青睐演唱经典、怀旧、民族及情感类歌曲的女主播如<|living_begin|>，并愿意为此打赏。此外，对历史人文、军旅文化、时事解析等知识型内容也有明确的付费和关注行为。 + +【行为模式】 +用户的消费偏好呈现出“高价值信任型”和“问题解决型”两大特征。在珠宝文玩领域，用户倾向于通过关注和打赏与主播建立信任，进而进行高客单价的购买。在健康养生领域，用户的购买行为则由明确的个人健康需求驱动。 + +跨域协同信号非常显著： +- **内容种草到电商拔草**：直播域的翡翠、助农产品内容直接导向了电商域的购买行为。 +- **需求驱动的全域探索**：对“耳鸣”的健康需求，驱动了用户在电商(购买理疗产品)、广告(点击医疗广告)和短视频(观看养生知识)的系列行为。 +- **情感共鸣驱动深度互动**：在情感关系领域的困惑，使用户在短视频和广告域产生了极高的互动粘性，表现为从被动浏览到主动评论、转发的深度参与。 + +【预测总结】 +综合分析，用户当前正处于对**情感关系问题**的高度关注期，其行为表现出强烈的信息渴求和情感寄托需求。 + +因此，预测用户接下来最可能感兴趣并**点击**的内容是**关于“情感挽回”、“两性心理分析”或“如何处理分手后社交关系”的短视频或文章**。 + +推断依据如下： +1. **行为的近期性和集中性**：在最近的交互记录中，此类内容的互动密度和深度(点赞、评论、长播、转发)远超其他类目，表明这是用户当前最迫切的兴趣点。 +2. **行为的情感投入度**：用户不仅观看，还进行评论和转发，这通常意味着内容触及其痛点，引发了强烈的情感共鸣和表达欲。 +3. **跨域行为的一致性**：用户对情感测试类广告的多次深度转化，进一步印证了其希望从外部寻求情感问题答案的心理状态。 + +所以，系统若推荐情感导师讲解、过来人经验分享或心理分析类的视频，可能会获得较高的点击率。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，基于您提供的用户交互历史，我将进行分析并提供我的推荐洞察。 + +【兴趣归纳】 +根据用户的多域行为，可以归纳出三大核心兴趣： +1. **动手实践与农耕生活**：这是用户最核心且投入最深的兴趣。用户在电商领域有大量且高频的购买行为(<|prod_begin|>、<|prod_begin|> 等)，集中于各类五金工具、电动设备、农用物资及种子。同时，在视频域，用户也多次关注、点赞、长播了关于农活技艺、工具制造与使用、动物养护(如修蹄)等内容(<|video_begin|>、 等)，形成了从工具购买到内容消费的完整闭环。 +2. **剧情类内容消费**：用户对短剧、动画等形式的剧情内容有广泛兴趣。其行为主要体现在广告域的深度转化(<|ad_begin|>、<|ad_begin|> 等)，内容题材偏好穿越、重生、乡土逆袭、家庭情感纠葛等。此外，用户的主动搜索行为(如搜索特定剧集)和对动画内容的浏览也印证了这一点。 +3. **接地气的地域与社会内容**：用户对具有浓厚生活气息和地域特色的内容表现出明显偏好。例如，在直播域关注了多位东北、内蒙等地的“接地气”主播(<|living_begin|>、<|living_begin|>)，内容涉及二手车、建材等实用领域。在视频域，用户也积极与社会百态、民生纪实类内容互动(<|video_begin|>、<|video_begin|>)。 + +【行为模式】 +用户的消费偏好呈现出典型的“实用主义”和“自给自足”的特点，消费决策高度集中于能够提升生产效率和解决实际问题的工具、设备及农用物资，而非时尚或享乐型商品。 +跨域协同信号非常强烈且一致： +- **“农与工”主题的协同**：用户在直播域关注二手车、建材主播，在电商域购买大量工农具，同时在视频域观看农耕、机械维修、手工制造等内容，形成了一条清晰的“看-买-学”行为链路。 +- **“剧情与现实”的融合**：用户消费的短剧内容(如重生回乡搞事业)与其现实中的农耕、动手兴趣高度相关，表明其在娱乐消费中也在寻求与自身生活经验或向往的共鸣。 +- **“地域与人物”的偏好**：用户对东北等北方地域文化和“接地气”、“实在”人设的偏好，贯穿了直播、视频和广告等多个消费场景。 + +【预测总结】 +综合分析，推测用户接下来最可能对以下两类内容产生高兴趣： +1. **硬核DIY与建造纪实类视频**：基于用户庞大的工具库和对动手实践的热衷，推荐如“用废旧材料自建温室”、“老式发动机翻新教程”或“传统木工/铁艺工艺揭秘”等内容，可能会获得用户的深度互动(如收藏、评论)。这类内容完美契合了用户的工具购买行为和求知、实践的内在动机。 +2. **融合了“基建/种田”元素的乡土短剧**：考虑到用户对剧情内容和农耕生活的双重兴趣，一部主角利用现代知识或特殊技能，在乡土环境中进行种植、养殖、建造或工业生产的短剧，很可能精准命中用户的“爽点”。这类内容将用户的现实兴趣与戏剧化叙事相结合，有较大几率促成其点击和追看。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +好的，收到您的指令。作为推荐系统分析助手，我将根据该用户的多域历史行为进行分析，并预测其潜在兴趣。 + +【兴趣归纳】 +根据用户的多域交互历史，可以归纳出三大核心兴趣类目： +1. **游戏内容(深度核心)**：这是用户最突出且投入最深的兴趣。行为贯穿直播、广告、视频、电商和搜索全域。在直播域，用户频繁关注并打赏游戏主播(<|living_begin|>、<|living_begin|>)，覆盖射击(如和平精英)、MOBA(如王者荣耀)、沙盒(如我的世界)和策略(如红色警戒)等多种类型，偏好技术流、教学向和高互动娱乐型主播。在电商域，用户多次购买游戏装备、周边及虚拟道具(<|prod_begin|>、<|prod_begin|>)。同时，用户对游戏广告有极高的点击和转化率(<|ad_begin|>、<|ad_begin|>)，显示出强烈的探索欲。 +2. **高颜值/陪伴型内容(高频兴趣)**：用户对高颜值主播，特别是女性主播，表现出持续的关注和付费意愿。其偏好标签包括“清纯气质”、“身材比例佳”、“甜美系”、“性感曲线”等(<|living_begin|>、<|living_begin|>)。除了外形，用户同样重视“互动陪伴”、“情感共鸣”和“真诚”等特质。该兴趣也体现在电商消费中，如购买美女主题的动态壁纸<|prod_begin|>和虚拟陪伴类游戏<|prod_begin|>。 +3. **娱乐搞笑/解压内容(稳定兴趣)**：用户对轻松、幽默、接地气的内容有稳定偏好。这体现在关注“搞笑互动”、“无厘头”、“整活”型主播(<|living_begin|>、<|living_begin|>)，观看搞笑视频，以及购买解压玩具<|prod_begin|>和零食<|prod_begin|>等行为上。 + +【行为模式】 +用户的消费偏好呈现出“为热爱付费”和“为情绪买单”的双重特征。他愿意为游戏优势(购买装备)、精神愉悦(打赏主播)和即时满足(购买零食、虚拟商品)进行消费。其付费行为集中在直播打赏和电商购物，尤其在游戏相关虚拟商品上表现出高复购率。 +跨域协同信号非常明显：用户的兴趣在不同场景下相互印证和加强。例如，观看游戏直播(直播域)会驱动其搜索游戏攻略(搜索域)、购买游戏道具(电商域)，并点击相关新游戏广告(广告域)。同样，对高颜值内容的偏好也从直播域延伸至视频消费和虚拟商品购买，形成了一个完整的“兴趣-探索-消费”闭环。 + +【预测总结】 +综合分析，用户接下来最可能感兴趣的内容大概率仍会聚焦于其核心兴趣领域。 +1. **游戏直播或视频**：特别是关于《和平精英》或《王者荣耀》的内容。一个“不露脸”但技术高超或解说风趣的教学型主播<|living_begin|>，或是一个展示高能操作、戏剧性翻盘的短视频<|video_begin|>，可能会吸引其点击。 +2. **高颜值互动内容**：一位兼具甜美外形与亲和互动特质的女性主播，无论是进行游戏直播还是才艺陪伴，都有较大几率获得用户的关注和互动。带有“二次元”、“纯欲风”、“JK”等特定视觉风格的虚拟主播或真人主播<|living_begin|>也可能引发其兴趣。 +推断依据是用户在这些领域的行为密度、互动深度(含付费)和跨域一致性最高，且近期行为(近三个月)依然高度集中于此，表明这些是其稳定且活跃的兴趣点。 +该用户最近首次打赏了主播: <|living_begin|><|im_end|> +好的，作为您的推荐系统分析助手，我已根据用户的多域历史行为进行了分析。以下是我的报告： + +### 【兴趣归纳】 +根据用户的交互历史，可以归纳出以下几个核心兴趣类目，其行为深度各不相同： + +1. **家庭生活与子女教育 (深度)**：这是用户最核心且投入最深的兴趣领域。行为贯穿商品、搜索和视频域。在**商品域**，用户有大量直接购买行为，覆盖了从K-12阶段的数学、语文、英语等教辅材料<|prod_begin|>, <|prod_begin|>)到儿童保健品<|prod_begin|>及儿童服饰与玩具。在**搜索域**，用户多次搜索与低年级教育相关的资源(如搜索“一年级题每日一练可打印”)。在**视频/广告域**，用户点击了儿童阅读平台<|ad_begin|>和育儿起名相关的广告，并关注家庭关系、教育理念的视频内容。 + +2. **短剧及影视娱乐 (深度)**：用户对短剧内容表现出极高的兴趣和粘性。在**广告域**，用户有超过20次针对各类短剧的点击和深度转化行为，题材高度集中于古装、奇幻、逆袭、言情等<|ad_begin|>, <|ad_begin|>、<|ad_begin|>)。这种高频的“深度转化”表明用户不仅被预告片吸引，还进行了观看、追剧等后续深入行为。在**视频域**，用户也主动消费与历史、情感、名人轶事相关的内容，这些是短剧的常见衍生或关联话题。 + +3. **实用生活与健康养生 (中到深度)**：该兴趣体现在解决实际生活问题和对健康的关注上。在**商品域**，用户购买了大量家居实用物品，如厨具<|prod_begin|>、收纳用品<|prod_begin|>、汽车配件<|prod_begin|>和家电维修零件<|prod_begin|>。同时，用户对养生保健有强烈需求，购买了多种传统滋补品、养生茶饮和保健书籍(<|prod_begin|>、<|prod_begin|>)。在**视频域**，用户收藏了多个烹饪教程<|video_begin|>和家居维修<|video_begin|>的视频，展现出强烈的学习和实践意愿。 + +### 【行为模式】 +用户的行为模式呈现出鲜明的**“家庭为中心”的务实型消费观**与**“娱乐驱动”的信息消费观**。 + +消费偏好上，用户是典型的家庭决策者，购买行为以满足家庭成员(尤其是孩子和长辈)的需求以及提升生活便利性为导向。其选购商品时注重实用性和性价比，例如购买厨房收纳、维修配件、多包装食品等。同时，用户有明确的“问题解决”导向，会通过搜索了解信息(如搜索汽车功能、家电信息)，然后进行相关消费(如购买汽车配件、浴霸)。 + +跨域协同信号非常显著： +* **视频/搜索驱动消费**：用户在视频域收藏烹饪、维修等“How-to”内容，并在商品域购买相关工具和食材，形成了从“学”到“用”的闭环。 +* **兴趣一致性**：用户对中医养生的兴趣同时体现在购买滋补品、养生书籍和浏览相关视频上。对子女教育的焦虑也同时反映在购买教辅、搜索学习资料和点击教育类广告上。 +* **娱乐内容消费路径**：用户通过广告被引入短剧，形成观看习惯后，会进一步消费相关的历史、情感类视频内容，展现了从被动接受到主动探索的兴趣深化路径。 + +### 【预测总结】 +综合分析，用户接下来最可能感兴趣并产生点击行为的内容，大概率是**短视频**形态，具体可细分为以下几类： + +1. **古装/家庭情感类短剧**：这是用户近期互动频率最高、付费转化意愿最强的类目。基于其对古装、逆袭、家庭纠葛等主题的持续“深度转化”行为<|ad_begin|>, <|ad_begin|>)，系统推荐具有相似元素的短剧切片或预告，有极大概率获得点击。 + +2. **生活窍门或烹饪教学类视频**：用户有多次收藏(<|video_begin|>、<|video_begin|>)此类视频的行为，表明其认为这类内容具有长期价值。因此，推送关于“家常菜新做法”、“旧物改造”、“家居收纳技巧”等实用性强的内容，很可能引发用户的收藏和观看行为。 + +3. **教育理念或社会热点解读类视频**：鉴于用户对子女教育的深度投入和对社会新闻的关注<|video_begin|>, <|video_begin|>)，推送有关“如何辅导孩子作业”、“解读最新教育政策”或近期社会热点事件的深度评论，可能会满足其信息获取和观点参考的需求，从而产生转发或评论互动。 +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +[INFO|configuration_utils.py:771] 2026-07-06 04:17:28,093 >> 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 04:17:28,094 >> 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 04:17:28] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-06 04:17:28] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-06 04:17:28,879 >> 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 04:17:28,879 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-06 04:17:28,929 >> 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 04:17:29,710 >> 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 04:17:30,027 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-06 04:17:30] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-06 04:17:30] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-06 04:17:30] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-06 04:17:30] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-06 04:17:30] 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 04:17:30,052 >> 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 04:17:30,168 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-06 04:17:30,168 >> Num examples = 1,112 +[INFO|trainer.py:1471] 2026-07-06 04:17:30,168 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-06 04:17:30,168 >> Num update steps per epoch = 278 +[INFO|trainer.py:1473] 2026-07-06 04:17:30,168 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-06 04:17:30,168 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-06 04:17:30,168 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-06 04:17:30,168 >> Total optimization steps = 278 +[INFO|trainer.py:1479] 2026-07-06 04:17:30,169 >> Number of trainable parameters = 801,433,600 + 0%| | 0/278 [00:00> `use_return_dict` is deprecated! 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/home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/checkpoint-278/config.json +[INFO|configuration_utils.py:816] 2026-07-06 04:53:37,853 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/checkpoint-278/generation_config.json +{'loss': '2.95', 'grad_norm': '24.75', 'learning_rate': '8.889e-06', 'epoch': '0.01799'} +{'loss': '2.495', 'grad_norm': '8.438', 'learning_rate': '2e-05', 'epoch': '0.03597'} +{'loss': '2.128', 'grad_norm': '3.875', 'learning_rate': '1.998e-05', 'epoch': '0.05396'} +{'loss': '1.933', 'grad_norm': '2.875', 'learning_rate': '1.993e-05', 'epoch': '0.07194'} +{'loss': '1.814', 'grad_norm': '2.281', 'learning_rate': '1.985e-05', 'epoch': '0.08993'} +{'loss': '1.74', 'grad_norm': '2.484', 'learning_rate': '1.973e-05', 'epoch': '0.1079'} +{'loss': '1.711', 'grad_norm': '2.188', 'learning_rate': '1.958e-05', 'epoch': '0.1259'} +{'loss': '1.664', 'grad_norm': '2.109', 'learning_rate': '1.939e-05', 'epoch': '0.1439'} +{'loss': '1.655', 'grad_norm': '1.977', 'learning_rate': '1.918e-05', 'epoch': '0.1619'} +{'loss': '1.621', 'grad_norm': '2.156', 'learning_rate': '1.893e-05', 'epoch': '0.1799'} +{'loss': '1.587', 'grad_norm': '2.109', 'learning_rate': '1.865e-05', 'epoch': '0.1978'} +{'loss': '1.591', 'grad_norm': '1.828', 'learning_rate': '1.834e-05', 'epoch': '0.2158'} +{'loss': '1.562', 'grad_norm': '1.992', 'learning_rate': '1.801e-05', 'epoch': '0.2338'} +{'loss': '1.56', 'grad_norm': '2.047', 'learning_rate': '1.764e-05', 'epoch': '0.2518'} +{'loss': '1.544', 'grad_norm': '2.016', 'learning_rate': '1.725e-05', 'epoch': '0.2698'} +{'loss': '1.523', 'grad_norm': '2.188', 'learning_rate': '1.684e-05', 'epoch': '0.2878'} +{'loss': '1.534', 'grad_norm': '2.203', 'learning_rate': '1.64e-05', 'epoch': '0.3058'} +{'loss': '1.519', 'grad_norm': '2.203', 'learning_rate': '1.594e-05', 'epoch': '0.3237'} +{'loss': '1.503', 'grad_norm': '2.062', 'learning_rate': '1.546e-05', 'epoch': '0.3417'} +{'loss': '1.528', 'grad_norm': '1.914', 'learning_rate': '1.497e-05', 'epoch': '0.3597'} +{'loss': '1.525', 'grad_norm': '1.914', 'learning_rate': '1.445e-05', 'epoch': '0.3777'} +{'loss': '1.509', 'grad_norm': '2.031', 'learning_rate': '1.392e-05', 'epoch': '0.3957'} +{'loss': '1.501', 'grad_norm': '2.188', 'learning_rate': '1.338e-05', 'epoch': '0.4137'} +{'loss': '1.478', 'grad_norm': '2.016', 'learning_rate': '1.282e-05', 'epoch': '0.4317'} +{'loss': '1.467', 'grad_norm': '2.125', 'learning_rate': '1.226e-05', 'epoch': '0.4496'} +{'loss': '1.489', 'grad_norm': '1.961', 'learning_rate': '1.169e-05', 'epoch': '0.4676'} +{'loss': '1.474', 'grad_norm': '2.016', 'learning_rate': '1.111e-05', 'epoch': '0.4856'} +{'loss': '1.469', 'grad_norm': '2.016', 'learning_rate': '1.053e-05', 'epoch': '0.5036'} +{'loss': '1.49', 'grad_norm': '2.25', 'learning_rate': '9.942e-06', 'epoch': '0.5216'} +{'loss': '1.464', 'grad_norm': '2.109', 'learning_rate': '9.358e-06', 'epoch': '0.5396'} +{'loss': '1.459', 'grad_norm': '2.047', 'learning_rate': '8.777e-06', 'epoch': '0.5576'} +{'loss': '1.48', 'grad_norm': '1.992', 'learning_rate': '8.2e-06', 'epoch': '0.5755'} +{'loss': '1.456', 'grad_norm': '1.969', 'learning_rate': '7.629e-06', 'epoch': '0.5935'} +{'loss': '1.465', 'grad_norm': '1.992', 'learning_rate': '7.066e-06', 'epoch': '0.6115'} +{'loss': '1.462', 'grad_norm': '2', 'learning_rate': '6.513e-06', 'epoch': '0.6295'} +{'loss': '1.449', 'grad_norm': '1.867', 'learning_rate': '5.972e-06', 'epoch': '0.6475'} +{'loss': '1.45', 'grad_norm': '1.961', 'learning_rate': '5.444e-06', 'epoch': '0.6655'} +{'loss': '1.459', 'grad_norm': '2.016', 'learning_rate': '4.933e-06', 'epoch': '0.6835'} +{'loss': '1.47', 'grad_norm': '1.969', 'learning_rate': '4.438e-06', 'epoch': '0.7014'} +{'loss': '1.496', 'grad_norm': '1.914', 'learning_rate': '3.963e-06', 'epoch': '0.7194'} +{'loss': '1.457', 'grad_norm': '1.984', 'learning_rate': '3.508e-06', 'epoch': '0.7374'} +{'loss': '1.47', 'grad_norm': '1.953', 'learning_rate': '3.075e-06', 'epoch': '0.7554'} +{'loss': '1.443', 'grad_norm': '2.219', 'learning_rate': '2.666e-06', 'epoch': '0.7734'} +{'loss': '1.465', 'grad_norm': '1.961', 'learning_rate': '2.281e-06', 'epoch': '0.7914'} +{'loss': '1.454', 'grad_norm': '2', 'learning_rate': '1.924e-06', 'epoch': '0.8094'} +{'loss': '1.482', 'grad_norm': '2.188', 'learning_rate': '1.593e-06', 'epoch': '0.8273'} +{'loss': '1.465', 'grad_norm': '1.766', 'learning_rate': '1.291e-06', 'epoch': '0.8453'} +{'loss': '1.462', 'grad_norm': '1.805', 'learning_rate': '1.019e-06', 'epoch': '0.8633'} +{'loss': '1.485', 'grad_norm': '1.898', 'learning_rate': '7.781e-07', 'epoch': '0.8813'} +{'loss': '1.481', 'grad_norm': '1.953', 'learning_rate': '5.681e-07', 'epoch': '0.8993'} +{'loss': '1.461', 'grad_norm': '2.047', 'learning_rate': '3.902e-07', 'epoch': '0.9173'} +{'loss': '1.457', 'grad_norm': '2.047', 'learning_rate': '2.452e-07', 'epoch': '0.9353'} +{'loss': '1.469', 'grad_norm': '1.969', 'learning_rate': '1.334e-07', 'epoch': '0.9532'} +{'loss': '1.47', 'grad_norm': '2.344', 'learning_rate': '5.519e-08', 'epoch': '0.9712'} +{'loss': '1.471', 'grad_norm': '1.867', 'learning_rate': '1.091e-08', 'epoch': '0.9892'} + + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/checkpoint-278/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 04:53:38,867 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/checkpoint-278/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 04:53:38,887 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/checkpoint-278/tokenizer_config.json +[INFO|trainer.py:1823] 2026-07-06 04:53:40,697 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 278/278 [36:10<00:00, 7.76s/it] 100%|██████████| 278/278 [36:10<00:00, 7.81s/it] +[INFO|trainer.py:3815] 2026-07-06 04:53:40,698 >> Saving model checkpoint to /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5 +[INFO|configuration_utils.py:533] 2026-07-06 04:53:40,699 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/config.json +[INFO|configuration_utils.py:816] 2026-07-06 04:53:40,699 >> Configuration saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/generation_config.json +{'train_runtime': '2171', 'train_samples_per_second': '0.512', 'train_steps_per_second': '0.128', 'train_loss': '1.575', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-06 04:53:41,520 >> chat template saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-06 04:53:41,541 >> tokenizer config file saved in /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 124335619GF + train_loss = 1.5747 + train_runtime = 0:36:10.52 + train_samples_per_second = 0.512 + train_steps_per_second = 0.128 +Figure saved at: /home/ll/llm4rec/experiments/20260706_overnight/outputs/v08_rec_focus_lr2e5/training_loss.png +[WARNING|2026-07-06 04:53:41] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-06 04:53:41] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-06 04:53:41,648 >> 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..f1232c5 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 1.335043548266926e+17, + "train_loss": 1.5747115251829298, + "train_runtime": 2170.5286, + "train_samples_per_second": 0.512, + "train_steps_per_second": 0.128 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..204aa01 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,56 @@ +{"current_steps": 5, "total_steps": 278, "loss": 2.950368309020996, "lr": 8.888888888888888e-06, "epoch": 0.017985611510791366, "percentage": 1.8, "elapsed_time": "0:00:39", "remaining_time": "0:36:12"} +{"current_steps": 10, "total_steps": 278, "loss": 2.4953432083129883, "lr": 2e-05, "epoch": 0.03597122302158273, "percentage": 3.6, "elapsed_time": "0:01:17", "remaining_time": 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