{"type":"ScholarlyArticle","title":"Sequential Recommender System based on Hierarchical Attention Networks","authors":[{"name":"Haochao Ying","slug":"haochao-ying","given":"Haochao","family":"Ying","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":"Key Lab of IIP of CAS , Institute of Computing Technology , CAS Beijing , China"},{"name":"Fuzheng Zhang","slug":"fuzheng-zhang","given":"Fuzheng","family":"Zhang","affiliation":"Microsoft Research , China"},{"name":"Yanchi Liu","slug":"yanchi-liu","given":"Yanchi","family":"Liu","affiliation":"Management Science & Information Systems , Rutgers University , USA"},{"name":"Guandong Xu","slug":"guandong-xu","given":"Guandong","family":"Xu","affiliation":"Advanced Analytics Institute , University of Technology , Australia; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research , China"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Management Science & Information Systems , Rutgers University , USA"},{"name":"Jian Wu","slug":"jian-wu","given":"Jian","family":"Wu","affiliation":"College of Computer Science and Technology , Zhejiang University , China"}],"abstract":"With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.","doi":"10.24963/ijcai.2018/546","url":"https://ijcai.org/proceedings/2018/546","pdf":"https://ijcai.org/proceedings/2018/0546.pdf","markdown":"https://ijcai.org/proceedings/2018/546.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/546","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":546,"pages":{"first":3926,"last":3932},"keywords":["Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bayer et al., 2017] Immanuel Bayer, Xiangnan He, Bhar- gav Kanagal, and Steffen Rendle. 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