Role-aware Conformity Influence Analysis in Recommender Systems

Recommender systems play an important role in providing personalized information to users and helping address the information overload problem. Recent research has considered social theories and studied the importance of social influence in social recommendation systems. However, many publications ignored the users' roles information or just considered some single roles. In fact, users often have many different roles. Besides, different types of users (users with different roles) might have different conformity tendency. Thus, this inspires us to study how conformity tendency changes with users' roles in recommender systems. We firstly formalize conformity influence by defining a utility function and then propose a probabilistic graphical model integrating both users' roles and conformity tendency, named as Role Conformity Recommender Systems (RCRS). We evaluate the proposed model on several real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches.