Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of supporting users who have very sparse interactions but post informative review texts. Our experimental studies address two book communities with these characteristics. We design a framework with Transformer-based representation learning, covering user-item interactions, item content, and user-provided reviews. To overcome interaction sparseness, we devise techniques for selecting the most informative cues to construct concise user profiles. Comprehensive experiments, with datasets from Amazon and Goodreads, show that judicious selection of text snippets achieves the best performance, even in comparison to ChatGPT-generated user profiles.

翻译：推荐系统在热门物品和具有丰富交互（点赞、评分等）的用户中最为成功。本研究针对用户交互稀疏但发布信息性评论文本这一困难且未被充分探索的案例进行探讨。我们的实验研究针对具有此类特征的两个图书社区展开。我们设计了一个基于Transformer的表示学习框架，涵盖用户-物品交互、物品内容以及用户提供的评论。为克服交互稀疏性问题，我们设计了选择最具信息性线索的技术，以构建简洁的用户画像。基于Amazon和Goodreads数据集的综合实验表明，即便与ChatGPT生成的用户画像相比，对文本片段进行明智选择仍能取得最佳性能。