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dc.contributor.authorBarraza-Urbina, Andrea
dc.contributor.authorHeitmann, Benjamin
dc.contributor.authorHayes, Conor
dc.contributor.authorCarrillo-Ramos, Angela
dc.date.accessioned2017-05-26T08:04:00Z
dc.date.available2017-05-26T08:04:00Z
dc.date.issued2015-07
dc.identifier.citationBarraza-Urbina, Andrea, Heitmann, Benjamin, Hayes, Conor, & Carrillo-Ramos, Angela. (2015) XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems Paper presented at the FLAIRS 2015, the 28th International Florida Artificial Intelligence Research Society Conference, Florida, 18/05/2015- 20/05/2017en_IE
dc.identifier.isbn978-1-57735-730-8
dc.identifier.urihttp://hdl.handle.net/10379/6548
dc.description.abstractRecommender Systems (RS) have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional RS usually do not produce diverse results though it has been argued that diversity is a desirable feature. The study of diversity-aware RS has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval (IR). However, we argue it is not enough to adapt IR techniques to RS as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to RS. In this work, we propose a diversification technique for RS that generates a diversified list of results which not only balances the trade-off between quality (in terms of accuracy) and diversity, but also considers the trade-off between exploitation of the user profile and exploration of novel products. Our experimental evaluation shows that the proposed approach has comparable results to state of the art approaches. In addition, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations.en_IE
dc.description.sponsorshipThis research was made possible by funding from Science Foundation Ireland under grant number SFI/12/RC/2289 (Insight) and by the Master's Program of the Computer Science Department at the Pontificia Universidad Javeriana, Bogotá.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherAAAI Pressen_IE
dc.relation.ispartofThe 28th International FLAIRS Conference 2015en
dc.subjectXPLODIVen_IE
dc.subjectRecommender systemsen_IE
dc.titleXPLODIV: An exploitation-exploration aware diversification approach for Recommender Systemsen_IE
dc.typeConference Paperen_IE
dc.date.updated2017-05-25T08:19:35Z
dc.local.publishedsourcehttps://aaai.org/Press/Proceedings/flairs15.phpen_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|1267883|~|
dc.internal.rssid12631424
dc.local.contactConor Hayes, Information Technology, School Of Engineering &, Informatics, Nui Galway. 5077 Email: conor.hayes@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionACCEPTED
nui.item.downloads211


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