{"type":"ScholarlyArticle","title":"Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning","authors":[{"name":"Zhou Zhao","slug":"zhou-zhao","given":"Zhou","family":"Zhao","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Lingtao Meng","slug":"lingtao-meng","given":"Lingtao","family":"Meng","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Jun Xiao","slug":"jun-xiao","given":"Jun","family":"Xiao","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Min Yang","slug":"min-yang","given":"Min","family":"Yang","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"},{"name":"Fei Wu","slug":"fei-wu","given":"Fei","family":"Wu","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Deng Cai","slug":"deng-cai","given":"Deng","family":"Cai","affiliation":"State Key Lab of CAD&CG , Zhejiang University"},{"name":"Xiaofei He","slug":"xiaofei-he","given":"Xiaofei","family":"He","affiliation":"State Key Lab of CAD&CG , Zhejiang University"},{"name":"Yueting Zhuang","slug":"yueting-zhuang","given":"Yueting","family":"Zhuang","affiliation":"College of Computer Science , Zhejiang University"}],"abstract":"Retweet prediction is a challenging problem in social\r\nmedia sites (SMS). In this paper, we study the problem\r\nof image retweet prediction in social media,\r\nwhich predicts the image sharing behavior that the\r\nuser reposts the image tweets from their followees.\r\nUnlike previous studies, we learn user preference\r\nranking model from their past retweeted image\r\ntweets in SMS. We first propose heterogeneous\r\nimage retweet modeling network (IRM) that\r\nexploits users' past retweeted image tweets with associated\r\ncontexts, their following relations in SMS\r\nand preference of their followees. We then develop\r\na novel attentional multi-faceted ranking network\r\nlearning framework with multi-modal neural networks\r\nfor the proposed heterogenous IRM network\r\nto learn the joint image tweet representations and\r\nuser preference representations for prediction\r\ntask. The extensive experiments on a large-scale\r\ndataset from Twitter site shows that our method\r\nachieves better performance than other state-of-the-art\r\nsolutions to the problem.","doi":"10.24963/ijcai.2018/442","url":"https://ijcai.org/proceedings/2018/442","pdf":"https://ijcai.org/proceedings/2018/0442.pdf","markdown":"https://ijcai.org/proceedings/2018/442.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/442","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":"Machine Learning","track":"Main track","number":442,"pages":{"first":3184,"last":3190},"keywords":["Machine Learning: Learning Preferences or Rankings","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Atrey et al., 2010] Pradeep K Atrey, M Anwar Hossain, Ab- dulmotaleb El Saddik, and Mohan S Kankanhalli. 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