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dc.contributor.authorWang, C
dc.contributor.authorSun, Z
dc.contributor.authorZhao, Y
dc.contributor.authorChi, CH
dc.contributor.authorvan den Heuvel, WJ
dc.contributor.authorLam, KY
dc.contributor.authorStantic, B
dc.date.accessioned2020-04-02T00:46:21Z
dc.date.available2020-04-02T00:46:21Z
dc.date.issued2019
dc.identifier.isbn9783030352301
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-35231-8_25
dc.identifier.urihttp://hdl.handle.net/10072/392906
dc.description.abstractConsidering the wide acceptance of the social media social influence starts to play very important role. Homophily has been widely accepted as the confounding factor for social influence. While literature attempts to identify and gauge the magnitude of the effects of social influence and homophily separately limited attention was given to use both sources for social behavior computing and prediction. In this work we address this shortcoming and propose neighborhood based collaborative filtering (CF) methods via the behavior interior dimensions extracted from the domain knowledge to model the data interdependence along time factor. Extensive experiments on the Twitter data demonstrate that the behavior interior based CF methods produce better prediction results than the state-of-the-art approaches. Furthermore, considering the impact of topic communication modalities (topic dialogicity, discussion intensiveness, discussion extensibility) on interior dimensions will lead to an improvement of 3%. Finally, the joint consideration of social influence and homophily leads to as high as 80.8% performance improvement in terms of accuracy when compared to the existing approaches.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencename15th International Conference on Advanced Data Mining and Applications (ADMA 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2019-11-21
dc.relation.ispartoflocationDalian, China
dc.relation.ispartofpagefrom343
dc.relation.ispartofpageto358
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11888
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode46
dc.titleTop-N Hashtag Prediction via Coupling Social Influence and Homophily
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationWang, C; Sun, Z; Zhao, Y; Chi, CH; van den Heuvel, WJ; Lam, KY; Stantic, B, Top-N Hashtag Prediction via Coupling Social Influence and Homophily, Advanced Data Mining and Applications, 2019, 11888, pp. 343-358
dc.date.updated2020-04-02T00:35:50Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can
gro.griffith.authorStantic, Bela


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