Deep learning techniques for recommender systems based on collaborative filtering

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorOhio State Univ-
Autor(es): dc.creatorMartins, Guilherme Brandao-
Autor(es): dc.creatorPapa, Joao Paulo [UNESP]-
Autor(es): dc.creatorAdeli, Hojjat-
Data de aceite: dc.date.accessioned2022-02-22T00:57:13Z-
Data de disponibilização: dc.date.available2022-02-22T00:57:13Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-11-13-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/exsy.12647-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209650-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209650-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionUniv Fed Sao Carlos, Dept Comp, Sao Carlos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil-
Descrição: dc.descriptionOhio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA-
Descrição: dc.descriptionOhio State Univ, Dept Neurosci, Columbus, OH 43210 USA-
Descrição: dc.descriptionSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.format21-
Idioma: dc.languageen-
Publicador: dc.publisherWiley-Blackwell-
Relação: dc.relationExpert Systems-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectcold start-
Palavras-chave: dc.subjectcollaborative filtering-
Palavras-chave: dc.subjectdeep learning-
Título: dc.titleDeep learning techniques for recommender systems based on collaborative filtering-
Aparece nas coleções:Repositório Institucional - Unesp

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