{"type":"ScholarlyArticle","title":"Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks","authors":[{"name":"Peizhi Wu","slug":"peizhi-wu","given":"Peizhi","family":"Wu","affiliation":"CCCE , Nankai University , Tianjin , China"},{"name":"Yi Tu","slug":"yi-tu","given":"Yi","family":"Tu","affiliation":"George Washington University , Washington , DC , USA"},{"name":"Xiaojie Yuan","slug":"xiaojie-yuan","given":"Xiaojie","family":"Yuan","affiliation":"CCCE , Nankai University , Tianjin , China"},{"name":"Adam Jatowt","slug":"adam-jatowt","given":"Adam","family":"Jatowt","affiliation":"Kyoto University , Kyoto , Japan"},{"name":"Zhenglu Yang","slug":"zhenglu-yang","given":"Zhenglu","family":"Yang","affiliation":"CCCE , Nankai University , Tianjin , China"}],"abstract":"Modeling the evolution of user feedback and social links in dynamic social networks is of considerable significance, because it is the basis of many applications, including recommendation systems and user behavior analyses. Most of the existing methods in this area model user behaviors separately and consider only certain aspects of this problem, such as dynamic preferences of users, dynamic attributes of items, evolutions of social networks, and their partial integration. This work proposes a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user feedback and social links. The framework considers the dynamic user preferences, dynamic item attributes, and time-dependent social links in time evolving social networks. Experimental results conducted on two real-world datasets demonstrate that our proposed model performs remarkably better than state-of-the-art methods.","doi":"10.24963/ijcai.2018/226","url":"https://ijcai.org/proceedings/2018/226","pdf":"https://ijcai.org/proceedings/2018/0226.pdf","markdown":"https://ijcai.org/proceedings/2018/226.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/226","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":"Humans and AI","track":"Main track","number":226,"pages":{"first":1632,"last":1638},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Neural Networks","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Crandall et al., 2008] David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, and Siddharth Suri. 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