Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8208
DC FieldValueLanguage
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2016-01-18T12:26:56Z-
dc.date.available2016-01-18T12:26:56Z-
dc.date.issued2013-
dc.identifier.citationCIKM'13 : proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 2013, Pages 2149-2158en
dc.identifier.issn978-1-4503-2263-8-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8208-
dc.descriptionPaper presented at 22nd ACM International Conference on Information & Knowledge Management, 2013, San Francisco, CA, USA, 27 October - 1 Novemberen
dc.description.abstractThe dramatic rates new digital content becomes available has brought collaborative filtering systems to the epicenter of computer science research in the last decade. One of the greatest challenges collaborative filtering systems are confronted with is the data sparsity problem: users typically rate only very few items; thus, availability of historical data is not adequate to effectively perform prediction. To alleviate these issues, in this paper we propose a novel multitask collaborative filtering approach. Our approach is based on a coupled latent factor model of the users rating functions, which allows for coming up with an agile information sharing mechanism that extracts much richer task-correlation information compared to existing approaches. Formulation of our method is based on concepts from the field of Bayesian nonparametrics, specifically Indian Buffet Process priors, which allow for data-driven determination of the optimal number of underlying latent features (item characteristics and user traits) assumed in the context of the model. We experiment on several real-world datasets, demonstrating both the efficacy of our method, and its superiority over existing approaches.en
dc.formatpdfen
dc.language.isoenen
dc.rights© ACMen
dc.subjectCollaborative filteringen
dc.subjectIndian buffet processen
dc.subjectMultitask learningen
dc.titleNonparametric bayesian multitask collaborative filteringen
dc.typeBook Chapteren
dc.collaborationCyprus University of Technology-
dc.subject.categoryComputer and Information Sciencesen
dc.reviewPeer Revieweden
dc.countryCyprus-
dc.subject.fieldNatural Sciencesen
dc.identifier.doi10.1145/2505515.2505517en
dc.dept.handle123456789/134en
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.openairetypebookPart-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
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