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dc.contributor.authorBarraza-Urbina, Andrea
dc.contributor.authord'Aquin, Mathieu
dc.date.accessioned2019-09-16T10:12:16Z
dc.date.available2019-09-16T10:12:16Z
dc.date.issued2019-09-20
dc.identifier.citationBarraza-Urbina, Andrea , & d’Aquin, Mathieu (2019). Towards sharing task environments to support reproducible evaluations of interactive recommender systems. Paper presented at the REVEAL’19, Copenhagen, Denmark, 20 September, DOI: 10.13025/pqjz-f728en_IE
dc.identifier.urihttp://hdl.handle.net/10379/15438
dc.description.abstractBeyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.en_IE
dc.description.sponsorshipThis publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2, cofunded by the European Regional Development Fund.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherNUI Galwayen_IE
dc.relation.ispartofBeyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.en
dc.subjectRecommender Systemsen_IE
dc.subjectRecommendation Systemsen_IE
dc.subjectReinforcement Learningen_IE
dc.subjectEvaluationen_IE
dc.subjectReproducibilityen_IE
dc.titleTowards sharing task environments to support reproducible evaluations of interactive recommender systemsen_IE
dc.typeWorkshop paperen_IE
dc.date.updated2019-09-16T10:04:19Z
dc.identifier.doi10.13025/pqjz-f728
dc.local.publishedsourcehttps://doi.org/10.13025/pqjz-f728
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.internal.rssid17627112
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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