Effects of User Interactions on Online Social Recommender Systems

We analyze online social data to model social interactions of users in recommender systems: i) Rating prediction, and ii) detecting spammers and abnormal user rating behaviors. We propose a social trust model using matrix factorization method to estimate users taste by incorporating user-item matrix. The effect of users friends tastes is modeled based on centrality metrics and similarity algorithms between users. The proposed method is validated using Epinions Dataset. To identify abnormal users in social recommender systems, we propose a classification approach. We define attributes to provide likelihood of a user having a profile of that of an attacker. Using user-item rating matrix and user-connection matrix, we find if the ratings are abnormal and if connections are random. We use k-means clustering to categorize users into authentic users and attackers. We use Epinions dataset to test the profile injection attacks.