Subprofile aware diversification of recommendations

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dc.contributor.advisor Bridge, Derek G. en
dc.contributor.advisor Brown, Kenneth N. en
dc.contributor.author Kaya, Mesut
dc.date.accessioned 2019-08-22T10:52:40Z
dc.date.available 2019-08-22T10:52:40Z
dc.date.issued 2019
dc.date.submitted 2019
dc.identifier.citation Kaya, M. 2019. Subprofile aware diversification of recommendations. PhD Thesis, University College Cork. en
dc.identifier.endpage 137 en
dc.identifier.uri http://hdl.handle.net/10468/8374
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 an aggregate 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 thesis, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification). In SPAD and its variants, the aspects are not item features; they are subprofiles of the user’s profile. We present 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 and its variants are useful even in domains where item features are not available or are of low quality. On several pre-collected datasets from different domains (movies, music, books, social network), we compare SPAD and its variants to intent-aware methods in which aspects are item features. We also compare them to calibrated recommendations, which are related to intent-aware recommendations. We find on these datasets that SPAD and its variants suffer even less from the relevance/diversity trade-off: across all datasets, they increase both relevance and diversity for even more configurations than other approaches. Moreover, we apply SPAD to the task of automatic playlist continuation (APC), in which relevance is the main goal, not diversity. We find that, even when applied to the task of APC, SPAD increases both relevance and diversity. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2019, Mesut Kaya. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Diversity en
dc.subject Subprofiles en
dc.subject Recommender systems en
dc.subject Intent-aware diversification en
dc.title Subprofile aware diversification of recommendations en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Computer Science and Information Technology en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor d.bridge@ucc.ie
dc.internal.conferring Autumn 2019 en
dc.internal.ricu Insight - Centre for Data Analytics 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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© 2019, Mesut Kaya. Except where otherwise noted, this item's license is described as © 2019, Mesut Kaya.
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