Personalised Ranking with Diversity

DC FieldValueLanguage
dc.contributor.authorHurley, Neil J.- ACMen_US
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.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)}
dc.subjectRecommender Systemsen_US
dc.subjectImplicit ratingsen_US
dc.titlePersonalised Ranking with Diversityen_US
dc.typeConference Publicationen_US
dc.statusPeer revieweden_US
dc.neeo.contributorHurley|Neil J.|aut|-
item.fulltextWith Fulltext-
Appears in Collections:Insight Research Collection
Files in This Item:
File Description SizeFormat 
Personalised Ranking with Diversity.pdf403.96 kBAdobe PDFDownload
Show simple item record

Page view(s)

Last Week
Last month
checked on Dec 9, 2019


checked on Dec 9, 2019

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.