用大模型构建与更新用户画像，开源数据集助力动态个性化研究
User Profile with Large Language Models: Construction, Updating, and Benchmarking
- 基于概率框架，用大模型从文本生成上下文感知的用户画像
- Mistral-7b和Llama2-7b在构建与更新任务中均表现优异
- 首次提供可公开测试的画像构建与更新双数据集，适合研究者使用
用户画像建模在个性化系统中至关重要，需准确构建并持续更新。本文提出两个高质量开源用户画像数据集：一个用于画像构建，另一个用于画像更新，为动态场景下的画像建模技术评估提供坚实基础。我们还提出一种利用大语言模型（LLMs）解决画像构建与更新的方法。该方法基于概率框架，从输入文本中预测用户画像，实现精准且上下文敏感的生成。实验表明，Mistral-7b 和 Llama2-7b 在两项任务中均表现强劲，显著提升生成画像的精确率与召回率，高评估分数验证了方法的有效性。
原文摘要 · Abstract (English)
User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong basis for evaluating user profile modeling techniques in dynamic settings. We also show a methodology that uses large language models (LLMs) to tackle both profile construction and updating. Our method uses a probabilistic framework to predict user profiles from input text, allowing for precise and context-aware profile generation. Our experiments demonstrate that models like Mistral-7b and Llama2-7b perform strongly in both tasks. LLMs improve the precision and recall of the generated profiles, and high evaluation scores confirm the effectiveness of our approach.
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