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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Social network data to alleviate cold-start in recommender system: A systematic review

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Gonzalez Camacho, Lesly Alejandra [1] ; Alves-Souza, Solange Nice [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] EPUSP, Dept Engn Comp & Sistemas Digitais, Av Prof Luciano Gualberto, Travessa 3, 158 Butanta, BR-05508010 Sao Paulo, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo de Revisão
Fonte: INFORMATION PROCESSING & MANAGEMENT; v. 54, n. 4, p. 529-544, JUL 2018.
Citações Web of Science: 11

Recommender Systems are currently highly relevant for helping users deal with the information overload they suffer from the large volume of data on the web, and automatically suggest the most appropriate items that meet users needs. However, in cases in which a user is new to Recommender System, the system cannot recommend items that are relevant to her/him because of lack of previous information about the user and/or the user-item rating history that helps to determine the users preferences. This problem is known as cold-start, which remains open because it does not have a final solution. Social networks have been employed as a good source of information to determine users preferences to mitigate the cold-start problem. This paper presents the results of a Systematic Literature Review on Collaborative Filtering-based Recommender System that uses social network data to mitigate the cold-start problem. This Systematic Literature Review compiled the papers published between 2011-2017, to select the most recent studies in the area. Each selected paper was evaluated and classified according to the depth which social networks used to mitigate the cold-start problem. The final results show that there are several publications that use the information of the social networks within the Recommender System; however, few research papers currently use this data to mitigate the cold start problem. (AU)

Processo FAPESP: 14/04851-8 - Big Data e Business Intelligence - uso de dados de redes sociais como solução para cenário de cold-start em sistema de recomendação
Beneficiário:Solange Nice Alves de Souza
Modalidade de apoio: Auxílio à Pesquisa - Regular