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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Gonzalez Camacho, Lesly Alejandra [1] ; Alves-Souza, Solange Nice [1]
Total Authors: 2
Affiliation:
[1] EPUSP, Dept Engn Comp & Sistemas Digitais, Av Prof Luciano Gualberto, Travessa 3, 158 Butanta, BR-05508010 Sao Paulo, SP - Brazil
Total Affiliations: 1
Document type: Review article
Source: INFORMATION PROCESSING & MANAGEMENT; v. 54, n. 4, p. 529-544, JUL 2018.
Web of Science Citations: 11
Abstract

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)

FAPESP's process: 14/04851-8 - Big Data and Business Intelligence - recommender system using social networks for cold-start issue
Grantee:Solange Nice Alves de Souza
Support type: Regular Research Grants