{"type":"ScholarlyArticle","title":"Learning Sequential Correlation for User Generated Textual Content Popularity Prediction","authors":[{"name":"Wen Wang","slug":"wen-wang","given":"Wen","family":"Wang","affiliation":"† Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"},{"name":"Wei Zhang","slug":"wei-zhang","given":"Wei","family":"Zhang","affiliation":"† Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"† Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"},{"name":"Junchi Yan","slug":"junchi-yan","given":"Junchi","family":"Yan","affiliation":"‡ Shanghai Jiao Tong University Georgia Institute of Technology"},{"name":"Hongyuan Zha","slug":"hongyuan-zha","given":"Hongyuan","family":"Zha","affiliation":""}],"abstract":"Popularity prediction of user generated textual content is critical for prioritizing information in the web, which alleviates heavy information overload for ordinary readers. Most previous studies model each content instance separately for prediction and thus overlook the sequential correlations between instances of a specific user. In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation. We propose a novel deep sequential model called User Memory-augmented recurrent Attention Network (UMAN). This model encodes the two correlations by updating external user memories which is further leveraged for target text representation learning and popularity prediction. The experimental results on several real-world datasets validate the benefits of considering these correlations and demonstrate UMAN achieves best performance among several strong competitors.","doi":"10.24963/ijcai.2018/225","url":"https://ijcai.org/proceedings/2018/225","pdf":"https://ijcai.org/proceedings/2018/0225.pdf","markdown":"https://ijcai.org/proceedings/2018/225.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/225","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":225,"pages":{"first":1625,"last":1631},"keywords":["Machine Learning: Recommender Systems","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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