Context-Aware Recommendations for Groups in Location-Based Social Networks and Academic Social Networks

Abstract

The impact of the World Wide Web goes beyond communicating between computers. The web changed the way we interact with organizations, people, documents, and data. Social media is one of the new buzzwords of the web. It is a comparatively new term that covers a wide range of online applications, platforms, and media that support online interactions, collaboration, and the sharing of content. It includes all that constitutes human interaction in online platforms. Social media has had grown explosively in a short time and has a profound impact on advertising, publicity, marketing, public opinion, entertainment, software services, and decision-making. The content offered in social networks is exciting to users. However, it is challenging, as users need to spend time and effort finding content that might be of their interest. Navigating such an extensive collection of items becomes difficult if there are no tools to help the users. To overcome this problem, social media relies on data mining solutions such as recommender systems and information retrieval systems. In particular, recommender systems have found applications in many domains including movies, music, videos, news, books, and products in general. Depending on the context they produce a list of recommended items using a variety of techniques. The goal of this dissertation is to research new approaches in recommender systems that help to satisfy the needs of users in social media. Specifically, we examine the role of recommender systems for users in two types of social media, Location-Based Social Networks and academic social networks. LBSNs enable their users to share the places they go to and with whom they are. Academic social networks help modern researchers organize their scientific libraries and discover relevant papers to their research. For both types of social media most of the existing work focuses on recommendations for individual users. The proposed approaches are distinguishable from others in that we focus on providing recommendations to a group of users, rather than to individuals. Moreover, we investigate the importance of item-to-item recommendations and propose a new method for recommending on infrequent items.