Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

中文翻译：

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CAESAR：基于对服务推荐的监督注意力的上下文感知解释

可解释推荐近来受到学术界和工业界的更多关注，因为它们可以帮助用户更好地理解推荐（即为什么推荐某些特定项目），从而提高推荐系统的说服力和用户的满意度。然而，从用户的上下文情况（例如，同伴、季节和目的地，如果推荐是酒店）的角度提供解释的工作很少。为了填补这一研究空白，我们提出了一种基于监督注意机制 (CAESAR) 的新上下文感知推荐算法，该算法特别将潜在特征与从用户生成的评论中挖掘的显式上下文特征匹配，以产生上下文感知解释。