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A Social Media Recommender System

A Social Media Recommender System

Giancarlo Sperlì, Flora Amato, Fabio Mercorio, Mario Mezzanzanica, Vincenzo Moscato, Antonio Picariello
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 15
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781522543787|DOI: 10.4018/IJMDEM.2018010103
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MLA

Sperlì, Giancarlo, et al. "A Social Media Recommender System." IJMDEM vol.9, no.1 2018: pp.36-50. http://doi.org/10.4018/IJMDEM.2018010103

APA

Sperlì, G., Amato, F., Mercorio, F., Mezzanzanica, M., Moscato, V., & Picariello, A. (2018). A Social Media Recommender System. International Journal of Multimedia Data Engineering and Management (IJMDEM), 9(1), 36-50. http://doi.org/10.4018/IJMDEM.2018010103

Chicago

Sperlì, Giancarlo, et al. "A Social Media Recommender System," International Journal of Multimedia Data Engineering and Management (IJMDEM) 9, no.1: 36-50. http://doi.org/10.4018/IJMDEM.2018010103

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Abstract

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.

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