基于大语言模型的用户画像管理在推荐系统中的应用

摘要

大型语言模型（LLMs）的快速发展为推荐系统开辟了新的机遇，使其能够实现无需传统训练的零样本推荐。尽管潜力巨大，但现有研究大多仅依赖用户的购买历史，通过整合用户生成的文本数据（如评论和产品描述）仍有显著提升空间。针对这一不足，我们提出了PURE，一种基于LLM的新型推荐框架，它通过系统性地提取和总结用户评论中的关键信息，构建并维护动态演化的用户画像。PURE包含三个核心组件：用于识别用户偏好和关键产品特征的评论提取器、用于精炼和更新用户画像的画像更新器，以及利用最新画像生成个性化推荐的推荐器。为评估PURE，我们引入了一项连续序列推荐任务，该任务通过随时间添加评论并逐步更新预测，反映了现实世界场景。在亚马逊数据集上的实验结果表明，PURE在有效利用长期用户信息的同时，妥善处理了令牌限制，其性能优于现有的基于LLM的方法。

English

The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.

基于大语言模型的用户画像管理在推荐系统中的应用

LLM-based User Profile Management for Recommender System

摘要

Summary

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