Personalised Ranking with Diversity

DC FieldValueLanguage
dc.contributor.authorHurley, Neil J.-
dc.date.accessioned2019-07-11T08:24:38Z-
dc.date.available2019-07-11T08:24:38Z-
dc.date.copyright2013 ACMen_US
dc.date.issued2013-10-16-
dc.identifier.isbn978-1-4503-2409-0-
dc.identifier.urihttp://hdl.handle.net/10197/10880-
dc.descriptionRecSys '13: 7th ACM conference on Recommender systems, Hong Kong, China, 12-16 October 2013en_US
dc.description.abstractIn this paper we discuss a method to incorporate diversity into a personalised ranking objective, in the context of ranking-based recommendation using implicit feedback. The goal is to provide a ranking of items that respects user preferences while also tending to rank diverse items closely together. A prediction formula is learned as the product of user and item feature vectors, in order to minimise the mean squared error objective used previously in the RankALS and RankSGD methods, but modified to weight the difference in ratings between two items by the dissimilarity of those items. We report on preliminary experiments with this modified objective, in which the minimisation is carried out using stochastic gradient descent. We show that rankings based on the output of the minimisation succeed in producing recommendation lists with greater diversity, with just a small loss in relevance of the recommendation, as measured by the error rate.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.relation.ispartofProceeding RecSys '13 Proceedings of the 7th ACM conference on Recommender systemsen_US
dc.rights© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/2507157.2507226en_US
dc.subjectRecommender Systemsen_US
dc.subjectDiversityen_US
dc.subjectImplicit ratingsen_US
dc.titlePersonalised Ranking with Diversityen_US
dc.typeConference Publicationen_US
dc.internal.webversionshttp://recsys.acm.org/recsys13/-
dc.statusPeer revieweden_US
dc.identifier.startpage379en_US
dc.identifier.endpage382en_US
dc.neeo.contributorHurley|Neil J.|aut|-
dc.date.updated2016-11-17T16:17:51Z-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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