{"type":"ScholarlyArticle","title":"A Review-Driven Neural Model for Sequential Recommendation","authors":[{"name":"Chenliang Li","slug":"chenliang-li","given":"Chenliang","family":"Li","affiliation":"School of Cyber Science and Engineering , Wuhan University , China"},{"name":"Xichuan Niu","slug":"xichuan-niu","given":"Xichuan","family":"Niu","affiliation":"School of Remote Sensing and Information Engineering , Wuhan University , China"},{"name":"Xiangyang Luo","slug":"xiangyang-luo","given":"Xiangyang","family":"Luo","affiliation":"State Key Lab of Mathematical Engineering and Advanced Computing , Zhengzhou , China"},{"name":"Zhenzhong Chen","slug":"zhenzhong-chen","given":"Zhenzhong","family":"Chen","affiliation":"School of Remote Sensing and Information Engineering , Wuhan University , China"},{"name":"Cong Quan","slug":"cong-quan","given":"Cong","family":"Quan","affiliation":"School of Computer Science , Wuhan University , China"}],"abstract":"Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering user's intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models.","doi":"10.24963/ijcai.2019/397","url":"https://ijcai.org/proceedings/2019/397","pdf":"https://ijcai.org/proceedings/2019/0397.pdf","markdown":"https://ijcai.org/proceedings/2019/397.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/397","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":397,"pages":{"first":2866,"last":2872},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Agrawal and Srikant, 1995] Rakesh Agrawal and Ramakr- ishnan Srikant. 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