Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)

Curiosity affects the users' selections of the items, and it motivates them to explore the items regardless of their preferences. This phenomenon is particularly common in the social networks. However, the existing social-based recommendation methods neglect users' curiosity in the social networks, and it may cause the accuracy decease in recommendation. What's more, only focusing on simulating the users' preferences can lead to information cocoons. In order to tackle the problems, we propose a Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model. Our model makes full use of the theories of psychology to model the users' curiosity aroused when facing different opinions. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.