ABSTRACT
The topic of social data and its potential applications have recently drawn substantial attention in a wide range of research disciplines. While the enormous amount and diverse forms of social data offer many great opportunities for the users of social platforms, it can still lead to undesired challenges such as Information Pollution in these platforms. This can cause the users of the social platforms to feel desperate when navigating in there and expose them to Choose Overload when making certain choices among an unlimited number of alternative ones. Recommender Systems are digital tools that can tackle these challenges through building personalization in social environments by exploiting the user preferences learned from the data that are produced by users through their online activities. This chapter focuses on personalization in social spaces built by a particular type of these systems, i.e., social recommender systems, integrated within social networks. It categorizes social recommender systems and provides a review of different types of these systems in the context of different application domains.
