ABSTRACT
Recommender systems are an effective solution to the information overload problem, especially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interest to users, we can classify the recommender systems into three major groups. First, content-based recommender systems use content information to make a recommendation. For example, such systems might recommend a romantic movie to a user who showed interest in romantic movies in her profile. Second, collaborative filtering recommender systems rely only on the past behavior of the users such as their previous transactions or ratings. By comparing this information, a collaborative filtering recommender system finds new items to users. In order to address the cold-start problem and fend off various types of attacks, the third class of recommender systems, namely trust-aware recommender systems, is proposed. These systems use social media and trust information to make a recommendation, which is shown to be promising in improving the accuracy of the recommendations. In this chapter, we give an overview of state-of-theart recommender systems with a focus on trust-aware recommender systems. In particular, we describe the ways that trust information can help to improve the quality of the recommendations. In the rest of the chapter, we introduce recommender systems, then trust in social media, and next trust-aware recommender systems.
