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dc.contributor.authorKotkov, Denis
dc.contributor.authorVeijalainen, Jari
dc.contributor.authorWang, Shuaiqiang
dc.date.accessioned2020-02-11T08:47:00Z
dc.date.available2020-02-11T08:47:00Z
dc.date.issued2020
dc.identifier.citationKotkov, D., Veijalainen, J., & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. <i>Computing</i>, <i>102</i>(2), 393-411. <a href="https://doi.org/10.1007/s00607-018-0687-5" target="_blank">https://doi.org/10.1007/s00607-018-0687-5</a>
dc.identifier.otherCONVID_28775637
dc.identifier.otherTUTKAID_79829
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67800
dc.description.abstractMost recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Wien
dc.relation.ispartofseriesComputing
dc.rightsCC BY 4.0
dc.subject.otherlearning to rank
dc.subject.otherserendipity
dc.subject.othernovelty
dc.subject.otherunexpectedness
dc.subject.otherserendipity-2018
dc.titleHow does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202002102023
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2020-02-10T07:15:13Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange393-411
dc.relation.issn0010-485X
dc.relation.numberinseries2
dc.relation.volume102
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2018
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber268078
dc.subject.ysoalgoritmit
dc.subject.ysosuosittelujärjestelmät
dc.subject.ysoarviointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p28483
jyx.subject.urihttp://www.yso.fi/onto/yso/p7413
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s00607-018-0687-5
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationOpen access funding provided by University of Jyväskylä (JYU). The research at the University of Jyväskylä was performed in the MineSocMed project, partially supported by the Academy of Finland, grant #268078 and the KAUTE Foundation.
dc.type.okmA1


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