Debiased offline evaluation of Active Learning in Recommender Systems

dc.contributor.authorCarraro, Diego
dc.contributor.authorBridge, Derek G.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2020-09-03T07:52:41Z
dc.date.available2020-09-03T07:52:41Z
dc.date.issued2020-05-08
dc.description.abstractActive Learning (AL) when applied to Recommender Systems (RSs) aims at proactively acquiring additional ratings data from the RS users in order to improve subsequent recommendation quality. AL strategies are typically evaluated offline first, but the classic AL offline evaluation methodology does not take into account the bias problem in RS offline evaluation. This problem affects the evaluation of an RS, as brought to light by recent literature. But, we argue, it also affects the evaluation of AL strategies as well. For this reason, in paper, we propose a new AL offline evaluation methodology for RSs which mitigates the bias and thus facilitates a truer picture of the performances of the AL strategies under evaluation. We illustrate our proposed methodology on two datasets and with three simple and well-known AL strategies from the literature. Our experimental results differ from those reported previously in the literature, which shows the importance of our approach to AL evaluation.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid494en
dc.identifier.citationCarraro, D. and Bridge, D. (2020) 'Debiased offline evaluation of Active Learning in Recommender Systems', Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), pp.489-494. Available at: https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18486 (Accessed: 3 September 2020)en
dc.identifier.endpage489en
dc.identifier.urihttps://hdl.handle.net/10468/10454
dc.language.isoenen
dc.publisherAssociation for the Advancement of Artificial Intelligenceen
dc.relation.urihttps://www.flairs-33.info/
dc.relation.urihttps://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18486/17639
dc.rights© 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The paper is posted here by permission of AAAI for your personal use. Not for redistribution.en
dc.subjectActive Learningen
dc.subjectRecommender Systemsen
dc.subjectOffline evaluationen
dc.titleDebiased offline evaluation of Active Learning in Recommender Systemsen
dc.typeConference itemen
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