Social Media Recommender Systems: Review and Open Research Issues

Abstract

In recent years, different types of review systems have been developed with the recommender system (RS). RSs are developed based on user textual reviews, ratings, and comparative opinions. RSs for social media resources, such as blogs, forums, social network websites, social bookmarking websites, video portals, and chat portals help users to collaborate effectively. Social media resources are used in the RS for recommending contents, articles, news, e-commerce products, and users. Although research on social media in RSs has increased annually, comprehensive literature review and classification of these RS studies are limited and must, therefore, be improved. This paper aims to provide a comprehensive review of the social media RS on research articles published from 2011 to 2015 by exploiting a methodological decision analysis in six aspects, including recommendation approaches, research domains, and data sets used in each domain, data mining techniques, recommendation type, and the use of performance measures. A total of 61 articles are reviewed among the initial 434 articles on RS research published in Web of Science and Scopus between 2011 and 2015. To accomplish the aim of this paper, a comprehensive review and analysis was performed on extracted articles to explore various recommendation approaches which are used in the RS. In addition, various social media domains are identified, where RSs have been employed. In each identified domain, publicly available data sets are also reported. Furthermore, various data mining techniques, recommendation types, and performance measures are also analyzed and reviewed in technical aspects. Finally, potential open research directions are also presented for future researchers intended to work in social media RS domain.