The recent decade introduced the “social web” or “Web 2.0” \cite{o2009web}, a web where people play a central role by creating content, annotating it with tags, votes (or ‘likes’), or comments, joining communities, and connecting to friends. Social media websites are proliferating and attract millions of users who author content, post messages, share photos with their friends, and engage in many other types of activities. This rapid growth intensifies the phenomenon of social overload, where users of social media are exposed to a huge amount of information and participate in huge amounts of interactions. Social overload makes it harder on the one hand for social media users to choose which sites to engage in and for how long and on the other hand makes it more challenging for social media websites to attract users and retain them.
Social Recommender Systems (SRS) are recommender systems that target the social media domain. They aim at coping with the social overload challenge by presenting the most relevant and attractive data to the user, typically by applying personalization techniques. The “marriage” between recommender systems (RS) and social media has many potential benefits for both sides. On the one hand, social media introduces many new types of data and meta-data, such as tags and explicit online relationships, which can be used in a unique manner by RS to enhance their effectiveness. On the other hand, recommender systems are crucial for social media websites to enhance the adoption and engagement by their users and thus play an important role in the overall success of social media. It should be noted that traditional RS, such as user-based collaborative filtering, are social in their nature since they mimic the natural process where we seek advice or suggestions from other people \cite{Resnick_1997}. Yet, in this chapter we focus on those recommender systems that are aimed for the social media domain, which we term social recommender systems \cite{Guy_2011}.
This chapter focuses on two key areas of SRS, social media content recommendation and people recommendation. We dedicate a section to each of these areas, reviewing the different sub-domains, their unique characteristics, the applied methods, case studies in the enterprise, and open challenges. SRS consist of more areas, such as recommendation of tags and groups (communities), however, these are left beyond the scope of this chapter. The remainder of the chapter is organized as follows. The next two sections discuss in detail content and people recommendation. The following section discusses key aspects characterizing SRS as raised throughout its preceding two sections. The chapter concludes by reviewing emerging SRS domains and open challenges.