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：本文探讨了一种基于文本语义相似性的新型用户偏好表示方法，用于评分预测。该方法将用户的偏好表示为评价文本中的文本片段图，其中边由文本间的语义相似度定义。这种基于文本的内存驻留方法使推荐结果得以使用评价文本解释。该方法经过量化评估，高亮显示这种利用文本的方法胜过了强内存驻留和模型驻留的协同过滤基线算法。