This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating prediction enables review-based explanations for recommendations. The method is evaluated quantitatively, highlighting that leveraging text in this way outperforms both strong memory-based and model-based collaborative filtering baselines.

翻译：本文探索了文本语义相似度在用户偏好表示中的新颖应用，用于评分预测。该方法将用户的偏好表示为来自评论文本的文本片段图，其中边由语义相似度定义。这种基于文本记忆的评分预测方法能够为推荐提供基于评论的解释。通过定量评估，本文强调，以这种方式利用文本优于强大的基于记忆和基于模型的协同过滤基线方法。