Hierarchical Multi-view Attention for Neural Review-Based Recommendation
Many E-commerce platforms allow users to write their opin-ions towards products,and these reviews contain rich semantic informa-tion for users and items.Hence review analysis has been widely used in recommendation systems.However,most existing review-based recom-mendation methods focus on a single view of reviews and ignore the diversity of users and items since users always have multiple prefer-ences and items always have various characteristics.In this paper,we propose a neural recommendation method with hierarchical multi-view attention which can effectively learn diverse user preferences and multi-ple item features from reviews.We design a review encoder with multi-view attention to learn representations of reviews from words,which can extract multiple points of a review.In addition,to learn representations of users and items from their reviews,we design a user/item encoder based on another multi-view attention.In this way,the diversity of user preference and item features can be fully exploited.Compared with the existing single attention approaches,the hierarchical multi-view atten-tion in our method has the potential for better user and product modeling from reviews.We conduct extensive experiments on four recommenda-tion datasets,and the results validate the advantage of our method for review based recommendation.
Recommender system Attention Review mining
Hongtao Liu Wenjun Wang Huitong Chen Wang Zhang Qiyao Peng Lin Pan Pengfei Jiao
College of Intelligence and Computing,Tianjin University,Tianjin,China College of Intelligence and Computing,Tianjin University,Tianjin,China;State Key Laboratory of Commu School of Marine Science and Technology,Tianjin University,Tianjin,China Center for Biosafety Research and Strategy,Law School,Tianjin University,Tianjin,China
国际会议
9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)
郑州
英文
1118-1129
2020-10-14（万方平台首次上网日期，不代表论文的发表时间）