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dc.contributor.authorPandey, Gaurav
dc.contributor.authorWang, Shuaiqiang
dc.contributor.authorRen, Zhaochun
dc.contributor.authorChang, Yi
dc.contributor.editorAzzopardi, Leif
dc.contributor.editorStein, Benno
dc.contributor.editorFuhr, Norbert
dc.contributor.editorMayr, Philipp
dc.contributor.editorHauff, Claudia
dc.contributor.editorHiemstra, Djoerd
dc.date.accessioned2019-12-18T08:00:24Z
dc.date.available2020-04-08T21:35:16Z
dc.date.issued2019
dc.identifier.citationPandey, G., Wang, S., Ren, Z., & Chang, Y. (2019). Vectors of Pairwise Item Preferences. In L. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff, & D. Hiemstra (Eds.), <i>ECIR 2019: Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–-8, 2019, Proceedings, Part I</i> (pp. 323-336). Springer. Lecture Notes in Computer Science, 11437. <a href="https://doi.org/10.1007/978-3-030-15712-8_21" target="_blank">https://doi.org/10.1007/978-3-030-15712-8_21</a>
dc.identifier.otherCONVID_29722441
dc.identifier.otherTUTKAID_81191
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/66917
dc.description.abstractNeural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users’ explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users’ pairwise item preferences as elementary units. It vectorizes users’ pairwise preferences by maximizing the likelihood estimation of the conditional probability of each pairwise item preference given another one. With the pairwise preference matrix and the generated preference vectors, the vectors of users are yielded by minimizing the difference between users’ observed preferences and the product of the user and preference vectors. Similarly, the vectorization of items can be achieved with the user-item rating matrix and the users vectors. We conducted extensive experiments on three benchmark datasets to assess the quality of item vectors and the initialization independence of the user and item vectors. The utility of our vectorization results is shown by the recommendation performance achieved using them. Our experimental results show significant improvement over state-of-the-art baselines.fi
dc.format.extent881
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofECIR 2019: Advances in Information Retrieval : 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–-8, 2019, Proceedings, Part I
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othervectorization
dc.subject.otherneural embedding
dc.titleVectors of Pairwise Item Preferences
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201912135255
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-12-13T10:15:28Z
dc.relation.isbn978-3-030-15711-1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange323-336
dc.relation.issn0302-9743
dc.relation.numberinseries11437
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Nature Switzerland AG 2019
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Conference on Information Retrieval
dc.subject.ysosuosittelujärjestelmät
dc.subject.ysoneuraalilaskenta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28483
jyx.subject.urihttp://www.yso.fi/onto/yso/p7291
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-15712-8_21


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