用用户评论动态构建画像，提升推荐系统长期记忆能力
LLM-based User Profile Management for Recommender System
- 从用户评论中提取偏好与产品特征，构建动态用户画像
- 在亚马逊数据集上优于现有方法，有效利用长期信息
- 适合需要持续更新用户兴趣的推荐场景
大型语言模型（LLM）的快速发展为推荐系统带来了零样本推荐的新机遇。然而，现有方法多依赖用户购买历史，未充分利用用户生成的文本数据（如评论和产品描述）。为此，我们提出PURE框架，通过系统性地从用户评论中提取并摘要关键信息，构建并维护演化的用户画像。PURE包含三个核心组件：评论提取器用于识别用户偏好和关键产品特征，画像更新器用于优化和更新用户画像，推荐器则基于最新画像生成个性化推荐。为评估该框架，我们设计了一项连续序列推荐任务，模拟真实场景中随时间增加评论并增量更新预测。在亚马逊数据集上的实验表明，PURE在性能上超越现有基于LLM的方法，能有效利用长期用户信息，同时应对令牌限制。
原文摘要 · Abstract (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.
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