{"type":"ScholarlyArticle","title":"Collaborative Self-Attention Network for Session-based Recommendation","authors":[{"name":"Anjing Luo","slug":"anjing-luo","given":"Anjing","family":"Luo","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Yanchi Liu","slug":"yanchi-liu","given":"Yanchi","family":"Liu","affiliation":"Rutgers University , New Jersey , USA"},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":"Key Lab of IIP of CAS , Institute of Computing Technology , Beijing , China; The University of Chinese Academy of Sciences , Beijing , China"},{"name":"Deqing Wang","slug":"deqing-wang","given":"Deqing","family":"Wang","affiliation":"School of Computer , Beihang University , Beijing , China"},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Junhua Fang","slug":"junhua-fang","given":"Junhua","family":"Fang","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Victor S. Sheng","slug":"victor-s-sheng","given":"Victor S.","family":"Sheng","affiliation":"Texas Tech University , Texas , USA"}],"abstract":"Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. However, existing session-based methods have the following limitations: (1) They either lack the capability to learn complex dependencies or focus mostly on the current session without explicitly considering collaborative information. (2) They assume that the representation of an item is static and fixed for all users at each time step. We argue that even the same item can be represented differently for different users at the same time step. To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions. Specially, we first devise a collaborative item representation by aggregating the embedding of neighborhood sessions retrieved according to each item in the current session. Then, we apply self-attention to learn long-range dependencies between collaborative items and generate collaborative session representation. Finally, each session is represented by concatenating the collaborative session representation and the embedding of the current session. Extensive experiments on two real-world datasets show that CoSAN constantly outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2020/359","url":"https://ijcai.org/proceedings/2020/359","pdf":"https://ijcai.org/proceedings/2020/0359.pdf","markdown":"https://ijcai.org/proceedings/2020/359.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/359","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":359,"pages":{"first":2591,"last":2597},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Deep Learning: Sequence Modeling"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24228/file_24228.m3u8","length_sec":674,"provider":"hls"}],"references":[{"position":1,"raw":"[Bonnin and Jannach, 2015] Geoffray Bonnin and Dietmar Jannach. 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