Recommender systems play an increasingly important role in the success of social media websites. Higher portions of social websites’ traffic are triggered by recommendations and those sites rely on the quality of the recommendations to attract new users and retain existing ones. In this chapter, we will introduce the notion of social recommender systems as recommender systems that target the social media domain. After a short introduction, we will discuss in detail two of the most prominent types of social recommender systems — recommendation of social media content and recommendation of people. We will describe the main approaches and state-of-the-art techniques for each of the recommendation types. We will also review related work from the recent years that studied such recommender systems, in order to demonstrate the different use cases and methods applied to take advantage of the unique data. We will conclude by summarizing the key aspects, emerging domains, and open challenges for social recommender systems.
The recent decade introduced the “social web” or “Web 2.0” (o’Reilly 2009), 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