Abstract
Many E-commerce platforms allow users to write their opinions towards products, and these reviews contain rich semantic information for users and items. Hence review analysis has been widely used in recommendation systems. However, most existing review-based recommendation methods focus on a single view of reviews and ignore the diversity of users and items since users always have multiple preferences 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 multiple 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 attention in our method has the potential for better user and product modeling from reviews. We conduct extensive experiments on four recommendation datasets, and the results validate the advantage of our method for review based recommendation.
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Acknowledgement
This work was supported by the National Key R&D Program of China (2018YFC0832101), the Tianjin Science and Technology Development Strategic Research Project under Grant (18ZXAQSF00110), National Social Science Foundation of China (15BGL035) and the Science and Technology Project for Livelihood of Qingdao (18-6-1-106-nsh).
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Liu, H. et al. (2020). Hierarchical Multi-view Attention for Neural Review-Based Recommendation. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_22
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DOI: https://doi.org/10.1007/978-3-030-60457-8_22
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