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dc.contributor.authorBatmaz, Zeynep
dc.contributor.authorYürekli, Ali
dc.contributor.authorBilge, Alper
dc.contributor.authorKaleli, Cihan
dc.date.accessioned2019-10-21T19:44:15Z
dc.date.available2019-10-21T19:44:15Z
dc.date.issued2019
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.urihttps://dx.doi.org/10.1007/s10462-018-9654-y
dc.identifier.urihttps://hdl.handle.net/11421/19840
dc.descriptionWOS: 000468576800001en_US
dc.description.abstractRecommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start. In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations. In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject. We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties. We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s10462-018-9654-yen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRecommender Systemsen_US
dc.subjectDeep Learningen_US
dc.subjectSurveyen_US
dc.subjectAccuracyen_US
dc.subjectScalabilityen_US
dc.subjectSparsityen_US
dc.titleA review on deep learning for recommender systems: challenges and remediesen_US
dc.typereviewen_US
dc.relation.journalArtificial Intelligence Reviewen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume52en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.endpage37en_US
dc.relation.publicationcategoryDiğeren_US]
dc.contributor.institutionauthorBatmaz, Zeynep
dc.contributor.institutionauthorBilge, Alper
dc.contributor.institutionauthorKaleli, Cihan


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