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Big Data and Business Intelligence - recommender system using social networks for cold-start issue

Grant number: 14/04851-8
Support type:Regular Research Grants
Duration: March 01, 2017 - August 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:Solange Nice Alves de Souza
Grantee:Solange Nice Alves de Souza
Home Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

In the last years have been observed a great increase in daily collected data from the internet, mobile devices and computer systems, which are practically used in all human activity. Great part of these data comes from social network, blogs, photos and videos, sensors, besides data maintained on servers that stored web browsing logs and any user relevant action on web sites. The Big Data term refers to these kinds of data and its management requirements, which is more complex due to the volume, variety and velocity that characterise these data. Using and take advantage of this enormous amount of data is extremely complex for human without the computer aid. Recommender Systems aims to help users to find artefacts (products, documents, information, services) that might interest them. Data users are employed to do recommendations. Cold-start is characterized by lack of user data and the recommendation is very difficult in this context. In spite of the researches found in literature, the cold-start problem is still a challenge. There is no a unique solution that could be employed in different domains or situations. In this scenario, data extracted from social networks allow to do assertive recommendations to the user. This project introduces the solution for cold-start using only data extracted from social networks. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GONZALEZ CAMACHO, LESLY ALEJANDRA; ALVES-SOUZA, SOLANGE NICE. Social network data to alleviate cold-start in recommender system: A systematic review. INFORMATION PROCESSING & MANAGEMENT, v. 54, n. 4, p. 529-544, . (14/04851-8)

Please report errors in scientific publications list by writing to: cdi@fapesp.br.