{"type":"ScholarlyArticle","title":"Deep Adversarial Social Recommendation","authors":[{"name":"Wenqi Fan","slug":"wenqi-fan","given":"Wenqi","family":"Fan","affiliation":"Department of Computer Science , City University of Hong Kong"},{"name":"Tyler Derr","slug":"tyler-derr","given":"Tyler","family":"Derr","affiliation":"Data Science and Engineering Lab , Michigan State University"},{"name":"Yao Ma","slug":"yao-ma","given":"Yao","family":"Ma","affiliation":"Data Science and Engineering Lab , Michigan State University"},{"name":"Jianping Wang","slug":"jianping-wang","given":"Jianping","family":"Wang","affiliation":"Department of Computer Science , City University of Hong Kong"},{"name":"Jiliang Tang","slug":"jiliang-tang","given":"Jiliang","family":"Tang","affiliation":"Data Science and Engineering Lab , Michigan State University"},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":"Department of Computing , The Hong Kong Polytechnic University"}],"abstract":"Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.","doi":"10.24963/ijcai.2019/187","url":"https://ijcai.org/proceedings/2019/187","pdf":"https://ijcai.org/proceedings/2019/0187.pdf","markdown":"https://ijcai.org/proceedings/2019/187.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/187","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":"Humans and AI","track":"Main track","number":187,"pages":{"first":1351,"last":1357},"keywords":["Humans and AI: Personalization and User Modeling","Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Brock et al., 2019] Andrew Brock, Jeff Donahue, and Karen Simonyan. 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