Deep learning techniques for recommender systems based on collaborative filtering

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dc.contributor.authorMartins, Guilherme Brandao
dc.contributor.authorPapa, Joao Paulo [UNESP]
dc.contributor.authorAdeli, Hojjat
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionOhio State Univ
dc.date.accessioned2021-06-25T12:25:01Z
dc.date.available2021-06-25T12:25:01Z
dc.date.issued2020-11-14
dc.description.abstractIn the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-the-art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.en
dc.description.affiliationUniv Fed Sao Carlos, Dept Comp, Sao Carlos, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.affiliationOhio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
dc.description.affiliationOhio State Univ, Dept Neurosci, Columbus, OH 43210 USA
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdCAPES: 001
dc.format.extent21
dc.identifierhttp://dx.doi.org/10.1111/exsy.12647
dc.identifier.citationExpert Systems. Hoboken: Wiley, v. 37, n. 6, 21 p., 2020.
dc.identifier.doi10.1111/exsy.12647
dc.identifier.issn0266-4720
dc.identifier.urihttp://hdl.handle.net/11449/209650
dc.identifier.wosWOS:000589093800001
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofExpert Systems
dc.sourceWeb of Science
dc.subjectcold start
dc.subjectcollaborative filtering
dc.subjectdeep learning
dc.titleDeep learning techniques for recommender systems based on collaborative filteringen
dc.typeResenha
dcterms.licensehttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dcterms.rightsHolderWiley-Blackwell
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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