Show simple item record

dc.contributor.authorBarraza-Urbina, Andrea
dc.contributor.authorHeitmann, Benjamin
dc.contributor.authorHayes, Conor
dc.contributor.authorRamos, Angela Carrillo
dc.date.accessioned2015-07-13T12:22:19Z
dc.date.available2015-07-13T12:22:19Z
dc.date.issued2015
dc.identifier.citationBarraza-Urbina, Andrea; Heitmann, Benjamin; Hayes, Conor; Ramos, Angela Carrillo; (2015) XploDiv: Diversification Approach for Recommender Systems. Galway, Ireland: Technical Publicationen_US
dc.identifier.urihttp://hdl.handle.net/10379/5081
dc.descriptionReporten_US
dc.description.abstractRecommender Systems 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 Recommender Systems usually do not produce diverse results, though it has been argued that diversity is a desirable feature. The study of diversity aware Recommender Systems has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval. However, we argue it is not enough to adapt Information Retrieval techniques towards Recommender Systems, as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to Recommender Systems. In this report, we propose a diversification technique for Recommender Systems 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, composed of both qualitative and quantitative tests, shows that the proposed approach has comparable results to state of the art approaches. Moreover, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations.en_US
dc.language.isoenen_US
dc.publisherINSIGHT Centre for Data Analytics, National University of Ireland, Galwayen_US
dc.subjectDiversityen_US
dc.subjectRecommender Systemsen_US
dc.subjectExploitationen_US
dc.subjectExplorationen_US
dc.subjectInformation Retrievalen_US
dc.subjectNoveltyen_US
dc.subjectDiscoveryen_US
dc.subjectRelevanceen_US
dc.titleXploDiv: Diversification Approach for Recommender Systemsen_US
dc.typeTechnical Reporten_US
dc.date.updated2015-05-05T16:19:39Z
dc.identifier.doi10.13025/S8PC74
dc.local.publishedsourcehttps://doi.org/10.13025/S8PC74
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funder|~|SFI|~|
dc.internal.rssid8721445
dc.local.contactBenjamin Heitmann, Insight, Ida Business Park, Nui Galway. Email: benjamin.heitmann@deri.org
dc.local.copyrightcheckedYes
dc.local.versionPUBLISHED
nui.item.downloads697


Files in this item

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

The following license files are associated with this item:

Thumbnail

This item appears in the following Collection(s)

Show simple item record