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Weighted Item ranking for pairwise matrix factorization

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dc.contributor.author Zhang, Haiyang
dc.contributor.author Ganchev, Ivan
dc.contributor.author Nikolov, Nikola S.
dc.contributor.author Ó'Droma, Máirtín
dc.date.accessioned 2019-04-03T09:10:07Z
dc.date.available 2019-04-03T09:10:07Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10344/7740
dc.description peer-reviewed en_US
dc.description.abstract Recommendation systems employed on the Internet aim to serve users by recommending items which will likely be of interest to them. The recommendation problem could be cast as either a rating estimation problem which aims to predict as accurately as possible for a user the rating values of items which are yet unrated by that user, or as a ranking problem which aims to find the top-k ranked items that would be of most interest to a user, which s/he has not ranked yet. In contexts where explicit item ratings of other users may not be available, the ranking prediction could be more important than the rating prediction. Most of the existing ranking-based prediction approaches consider items as having equal weights which is not always the case. Different weights of items could be regarded as a reflection of items’ importance, or desirability, to users. In this paper, we propose to integrate variable item weights with a ranking-based matrix factorization model, where learning is driven by Bayesian Personalized Ranking (BPR). Two ranking-based models utilizing different-weight learning methods are proposed and the performance of both models is confirmed as being better than the standard BPR method. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries 2017 South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM);
dc.relation.uri http://dx.doi.org/10.23919/SEEDA-CECNSM.2017.8089996
dc.rights © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en_US
dc.subject collaborative filtering en_US
dc.subject matrix factorization en_US
dc.subject Bayesian personalized ranking en_US
dc.subject implicit feedback en_US
dc.subject item recommendation en_US
dc.title Weighted Item ranking for pairwise matrix factorization en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.23919/SEEDA-CECNSM.2017.8089996
dc.contributor.sponsor Chinese Scholarship Council en_US
dc.contributor.sponsor Telecommunications Research Centre (TRC) University of Limerick en_US
dc.contributor.sponsor University of Plovdiv en_US
dc.relation.projectid ФП17-ФМИ-008 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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