AOPUT: A recommendation framework based on social activities and content interests

Content consuming and sharing are two most important user activities in social networking sites (SNSs). Lots of studies have been conducted on content recommendation using users´ common interests. However, little has been done to help users to select friends and share content within their social networks. In this paper, we contribute a recommendation framework AOPUT to recommend both content and friend list for sharing to users leveraging content and social information in SNSs. It consists of two recommendation components: Recder and ShareAider. Recder generates content recommendations by connecting users with common interests. An improved Jaccard similarity is proposed to improve the Collaborative Filtering (CF) recommendation quality. ShareAider recommends a friend list to users when they want to share content with their friends. CF method and a social-based method are compared and the combination of them are explored to achieve better results. AOPUT is evaluated on a real world social network. The experimental results show that (1) Recder can provide better recommendation quality than the traditional CF method thanks to the improved Jaccard similarity; (2) social-based method performs better than CF since the sharing behavior in SNSs are highly dominated by users´ social preferences, and the combination of these two methods performs better than each of them individually.