Estimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systems

dc.contributor.authorCoscrato, Victor
dc.contributor.authorBridge, Derek G.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-03-09T11:56:35Z
dc.date.available2023-03-09T11:56:35Z
dc.date.issued2023-02-16
dc.date.updated2023-02-22T15:04:30Z
dc.description.abstractUncertainty is a characteristic of every data-driven application, including recommender systems. The quantification of uncertainty can be key to increasing user trust in recommendations or choosing which recommendations should be accompanied by an explanation; and uncertainty estimates can be used to accomplish recommender tasks such as active learning and co-training. Many uncertainty estimators are available but, to date, the literature has lacked a comprehensive survey and a detailed comparison. In this paper, we fulfil these needs. We review the existing methods for uncertainty estimation and metrics for evaluating uncertainty estimates, while also proposing some estimation methods and evaluation metrics of our own. Using two datasets, we compare the methods using the evaluation metrics that we describe, and we discuss their strengths and potential issues. The goal of this work is to provide a foundation to the field of uncertainty estimation in recommender systems, on which further research can be built.en
dc.description.sponsorshipScience Foundation Ireland (18/CRT/6223000)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid7en
dc.identifier.citationCoscrato, V. and Bridge, D. (2023) ‘Estimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systems’, ACM Transactions on Recommender Systems, 1(2), pp. 1–34. Available at: https://doi.org/10.1145/3584021en
dc.identifier.doihttps://doi.org/10.1145/3584021en
dc.identifier.eissn2770-6699
dc.identifier.endpage34en
dc.identifier.journaltitleACM Transactions on Recommender Systemsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14296
dc.identifier.volume1en
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.rights© 2023, the Authors. Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Recommender Systems, https://doi.org/10.1145/3584021en
dc.subjectInformation systemsen
dc.subjectRecommender systemsen
dc.subjectUncertaintyen
dc.subjectComputing methodologiesen
dc.subjectUncertainty quantificationen
dc.subjectGeneral and referenceen
dc.subjectEmpirical studiesen
dc.subjectEvaluationen
dc.subjectEstimationen
dc.subjectMathematics of computingen
dc.subjectProbability and statisticsen
dc.subjectUncertaintyen
dc.subjectRecommender systemsen
dc.titleEstimating and evaluating the uncertainty of rating predictions and top-n recommendations in recommender systemsen
dc.typeArticle (peer-reviewed)en
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