Subprofile-aware diversification of recommendations

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dc.contributor.author Kaya, Mesut
dc.contributor.author Bridge, Derek
dc.date.accessioned 2019-07-04T11:46:51Z
dc.date.available 2019-07-04T11:46:51Z
dc.date.issued 2019-04-20
dc.identifier.citation Kaya, M. and Bridge, D. (2019) 'Subprofile-aware diversification of recommendations', User Modeling and User-Adapted Interaction, pp. 1-40. doi: 10.1007/s11257-019-09235-6 en
dc.identifier.startpage 1 en
dc.identifier.endpage 40 en
dc.identifier.issn 1573-1391
dc.identifier.uri http://hdl.handle.net/10468/8114
dc.identifier.doi 10.1007/s11257-019-09235-6 en
dc.description.abstract A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by the average of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this paper, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification), and a variant called RSPAD (Relevance-based SPAD). In SPAD, the aspects are not item features; they are subprofiles of the user’s profile. We present and compare a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD is useful even in domains where item features are not available or are of low quality. On three pre-collected datasets from three different domains (movies, music artists and books), we compare SPAD and RSPAD to intent-aware methods in which aspects are item features. We find on these datasets that SPAD and RSPAD suffer even less from the relevance/diversity trade-off: across all three datasets, they increase both relevance and diversity for even more configurations than other approaches to diversification. Moreover, we find that SPAD and RSPAD are the most accurate systems across all three datasets. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer Verlag en
dc.relation.uri https://link.springer.com/article/10.1007%2Fs11257-019-09235-6
dc.rights © Springer Nature B.V. 2019. This is a post-peer-review, pre-copyedit version of an article published in User Modeling and User-Adapted Interaction. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11257-019-09235-6 en
dc.subject Recommender systems en
dc.subject Diversity en
dc.subject Intent-aware diversification en
dc.subject Subprofiles en
dc.title Subprofile-aware diversification of recommendations en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Mesut Kaya, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: mesut.kaya@insight-centre.org en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher. en
dc.check.date 2020-04-20
dc.date.updated 2019-07-04T11:37:41Z
dc.description.version Accepted Version en
dc.internal.rssid 491554811
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.identifier.journaltitle User Modeling and User-Adapted Interaction en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress d.bridge@cs.ucc.ie en
dc.internal.IRISemailaddress mesut.kaya@insight-centre.org en
dc.internal.bibliocheck In Press. Update citation, add vol. issue, update page nos. en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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