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dc.contributor.authorKotkov, Denis
dc.contributor.authorWang, Shuaiqiang
dc.contributor.authorVeijalainen, Jari
dc.contributor.editorMonfort, Valérie
dc.contributor.editorKrempels, Karl-Heinz
dc.contributor.editorMajchrzak, Tim A.
dc.contributor.editorTraverso, Paolo
dc.date.accessioned2017-10-13T10:18:28Z
dc.date.available2018-09-09T21:35:51Z
dc.date.issued2017
dc.identifier.citationKotkov, D., Wang, S., & Veijalainen, J. (2017). Improving Serendipity and Accuracy in Cross-Domain Recommender Systems. In V. Monfort, K.-H. Krempels, T. A. Majchrzak, & P. Traverso (Eds.), <i>WEBIST 2016 : the 12th International conference on web information systems and technologies. Revised Selected Papers</i> (pp. 105-119). Springer International Publishing AG. Lecture Notes in Business Information Processing, 292. <a href="https://doi.org/10.1007/978-3-319-66468-2_6" target="_blank">https://doi.org/10.1007/978-3-319-66468-2_6</a>
dc.identifier.otherCONVID_27260260
dc.identifier.otherTUTKAID_75200
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55627
dc.description.abstractCross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. According to our results, the source domain can improve serendipity in the target domain for both approaches. The source domain decreases accuracy for contentbased filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains.
dc.format.extent185
dc.language.isoeng
dc.publisherSpringer International Publishing AG
dc.relation.ispartofWEBIST 2016 : the 12th International conference on web information systems and technologies. Revised Selected Papers
dc.relation.ispartofseriesLecture Notes in Business Information Processing
dc.subject.otherserendipity
dc.subject.othercross-domain recommendations
dc.subject.othercollaborative filtering
dc.subject.othercontent-based filtering
dc.subject.otherdata collection
dc.titleImproving Serendipity and Accuracy in Cross-Domain Recommender Systems
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201710103972
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-10-10T09:15:07Z
dc.relation.isbn978-3-319-66467-5
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange105-119
dc.relation.issn1865-1348
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational conference on web information systems and technologies
dc.relation.grantnumber268078
dc.subject.ysosuosittelujärjestelmät
jyx.subject.urihttp://www.yso.fi/onto/yso/p28483
dc.relation.doi10.1007/978-3-319-66468-2_6
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThe research at the University of Jyväskylä was performed in the MineSocMed project, partially supported by the Academy of Finland, grant #268078. The communication of this research was supported by Daria Wadsworth.


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