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dc.contributor.authorBarraza-Urbina, Andrea
dc.contributor.authorKoutrika, Georgia
dc.contributor.authord'Aquin, Mathieu,
dc.contributor.authorHayes, Conor
dc.date.accessioned2019-09-16T11:47:58Z
dc.date.available2019-09-16T11:47:58Z
dc.date.issued2018-10-06
dc.identifier.citationBarraza-Urbina, Andrea , Koutrika, Georgia , d‘Aquin, Mathieu , & Hayes, Conor (2018). BEARS: Towards an evaluation framework for bandit-based interactive recommender systems. Paper presented at the REVEAL’18, Vancouver, Canada, 06-07 October, DOI: 10.13025/x72s-8r20en_IE
dc.identifier.urihttp://hdl.handle.net/10379/15439
dc.description.abstractRecommender Systems (RS) deployed in fast-paced dynamic scenarios must quickly learn to adapt in response to user evaluative feedback. In these settings, the RS faces an online learning problem where each decision should optimize two competing goals: gather new information about users and optimally serve users according to acquired knowledge. Related works commonly address this exploration-exploitation trade-off by proposing bandit-based RS. However, evaluating bandit-based RS in an offline interactive environment remains an open challenge. This paper presents BEARS, an evaluation framework that allows users to easily test bandit-based RS solutions. BEARS aims to support reproducible offline evaluations by providing simple building blocks for constructing experiments in a shared platform. Moreover, BEARS can be used to share benchmark problem settings (Environments) and reusable implementations of baseline solution approaches (RS Agents).en_IE
dc.description.sponsorshipThis publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherNUI Galwayen_IE
dc.relation.ispartofREVEAL 18, October 6-7, 2018, Vancouver, Canadaen
dc.subjectBandit-baseden_IE
dc.subjectInteractive Recommender Systemsen_IE
dc.subjectRecommender Systems (RS)en_IE
dc.titleBEARS: Towards an evaluation framework for bandit-based interactive recommender systemsen_IE
dc.typeConference Paperen_IE
dc.date.updated2019-09-16T11:22:11Z
dc.identifier.doi10.13025/x72s-8r20
dc.local.publishedsourcehttps://doi.org/10.13025/x72s-8r20
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.internal.rssid17678139
dc.local.contactAndrea Barraza, Insight Centre For Data Analytics, Ida Business Park, Lower Dangan, Galway. Email: a.barraza1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
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