Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85173
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dc.contributor.authorQiu, Huihuaien
dc.contributor.authorLiu, Yunen
dc.contributor.authorGuo, Guibingen
dc.contributor.authorSun, Zhuen
dc.contributor.authorZhang, Jieen
dc.contributor.authorNguyen, Hai Thanhen
dc.date.accessioned2019-07-08T07:46:58Zen
dc.date.accessioned2019-12-06T15:58:42Z-
dc.date.available2019-07-08T07:46:58Zen
dc.date.available2019-12-06T15:58:42Z-
dc.date.issued2018en
dc.identifier.citationQiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., & Nguyen, H. T. (2018). BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Information Sciences, 453, 80-98. doi:10.1016/j.ins.2018.04.027en
dc.identifier.issn0020-0255en
dc.identifier.urihttps://hdl.handle.net/10356/85173-
dc.description.abstractPersonalized recommendation for online service systems aims to predict potential demand by analysing user preference. User preference can be inferred from heterogeneous implicit feedback (i.e. various user actions) especially when explicit feedback (i.e. ratings) is not available. However, most methods either merely focus on homogeneous implicit feedback (i.e. target action), e.g., purchase in shopping websites and forward in Twitter, or dispose heterogeneous implicit feedback without the investigation of its speciality. In this paper, we adopt two typical actions in online service systems, i.e., view and like, as auxiliary feedback to enhance recommendation performance, whereby we propose a Bayesian personalized ranking method for heterogeneous implicit feedback (BPRH). Specifically, items are first classified into different types according to the actions they received. Then by analysing the co-occurrence of different types of actions, which is one of the fundamental speciality of heterogeneous implicit feedback systems, we quantify their correlations, based on which the difference of users’ preference among different types of items is investigated. An adaptive sampling strategy is also proposed to tackle the unbalanced correlation among different actions. Extensive experimentation on three real-world datasets demonstrates that our approach significantly outperforms state-of-the-art algorithms.en
dc.format.extent44 p.en
dc.language.isoenen
dc.relation.ispartofseriesInformation Sciencesen
dc.rights© 2018 Elsevier Inc. All rights reserved. This paper was published in Information Sciences and is made available with permission of Elsevier Inc.en
dc.subjectRecommendationen
dc.subjectHeterogeneous Implicit Feedbacken
dc.subjectEngineering::Computer science and engineeringen
dc.titleBPRH : bayesian personalized ranking for heterogeneous implicit feedbacken
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1016/j.ins.2018.04.027en
dc.description.versionAccepted versionen
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