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 LLM-generated rankings and to using LLMs to generate user profiles.

翻译：推荐系统在拥有丰富交互（点赞、评分等）的热门物品与用户中表现最为出色。本文针对用户交互稀疏但发布具有信息性评论文本这一难以处理且尚未充分研究的场景展开研究。我们针对两个具有此类特征的图书社区开展实验研究，设计了一个基于Transformer表示学习的框架，涵盖用户-物品交互、物品内容及用户评论。为克服交互稀疏性问题，我们提出了选取最具信息性线索的技巧，以构建简洁的用户画像。基于Amazon和Goodreads数据集的全面实验表明，精心选择文本片段能达到最优性能，甚至优于基于大语言模型生成的排序结果及使用大语言模型生成用户画像的方法。