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A Social Media Recommender System
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Author(s): Giancarlo Sperlì (University of Naples “Federico II”, Naples, Italy), Flora Amato (University of Naples “Federico II”, Naples, Italy), Fabio Mercorio (Department of Statistics and Quantitative Methods Crisp Research Centre, University of Milan-Bicocca, Milan, Italy), Mario Mezzanzanica (Department of Statistics and Quantitative Methods Crisp Research Centre, University of Milan-Bicocca, Milan, Italy), Vincenzo Moscato (University of Naples “Federico II”, Naples, Italy)and Antonio Picariello (University of Naples “Federico II”, Naples, Italy)
Copyright: 2018
Volume: 9
Issue: 1
Pages: 15
Source title:
International Journal of Multimedia Data Engineering and Management (IJMDEM)
Editor(s)-in-Chief: Chengcui Zhang (University of Alabama at Birmingham, USA)and Shu-Ching Chen (University of Missouri-Kansas City, United States)
DOI: 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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