Combining novelty and popularity on personalised recommendations via user profile learning

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dc.contributor.authorBERTANI, RICARDO MITOLLO
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorCOSTA, Anna Helena Reali
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2021-11-24T20:29:58Z
dc.date.available2021-11-24T20:29:58Z
dc.date.issued2020-05-15
dc.description.abstractRecommender systems have been widely used by large companies in the e-commerce segment as aid tools in the search for relevant contents according to the user’s particular preferences. A wide variety of algorithms have been proposed in the literature aiming at improving the process of generating recom- mendations; in particular, a collaborative, diffusion-based hybrid algorithm has been proposed in the lit- erature to solve the problem of sparse data, which affects the quality of recommendations. This algorithm was the basis for several others that effectively solved the sparse data problem. However, this family of algorithms does not differentiate users according to their profiles. In this paper, a new algorithm is pro- posed for learning the user profile and, consequently, generating personalised recommendations through diffusion, combining novelty with the popularity of items. Experiments performed in well-known datasets show that the results of the proposed algorithm outperform those from both diffusion-based hybrid al- gorithm and traditional collaborative filtering algorithm, in the same settings.
dc.description.firstpage113149
dc.description.volume146
dc.identifier.citationBERTANI, R. M.; BIANCHI, R. A. DA C. ;COSTA, A. H. R. Combining novelty and popularity on personalised recommendations via user profile learning. EXPERT SYSTEMS WITH APPLICATIONS, v. 146, p. 113149, 2020.
dc.identifier.doi10.1016/j.eswa.2019.113149
dc.identifier.issn0957-4174
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3474
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.rightsAcesso Restrito
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY-NC-ND 4.0). Fonte: https://www.sciencedirect.com/science/article/pii/S0957417419308668?via%3Dihub. Acesso em: 24 nov. 2021.
dc.subjectRecommender systems
dc.subjectMachine learning
dc.subjectData sparsity
dc.subjectDiffusion-based algorithms
dc.subjectUser profile
dc.titleCombining novelty and popularity on personalised recommendations via user profile learningpt_BR
dc.typeArtigopt_BR
fei.scopus.citations21
fei.scopus.eid2-s2.0-85077430544
fei.scopus.updated2024-05-01
fei.source.urlhttps://www.sciencedirect.com/science/article/pii/S0957417419308668?via%3Dihub
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