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Title: Combining novelty and popularity on personalised recommendations via user profile learning
Reinaldo Bianchi
COSTA, Anna Helena Reali
Issue Date: 15-May-2020
Abstract: Recommender 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.
Keywords: Recommender systems
Machine learning
Data sparsity
Diffusion-based algorithms
User profile
ISSN: 0957-4174
Citation: BERTANI, 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.
Access Type: Acesso Aberto
DOI: 10.1016/j.eswa.2019.113149
Appears in Collections:Artigos

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