Curiosity Enhanced Bayesian Personalized Ranking for Recommender Systems

Yaoming Deng, Qiqi Ding, Xin Wu, Yi Cai

Abstract: Curiosity affects the users’ selections of items, motivating them to explore the items regardless of their preferences. However, the existing social-based recommendation methods neglect the users’ curiosity in the social networks, and it may cause the accuracy decrease in the recommendation. Moreover, only focusing on simulating the users’ preferences can lead to users’ information cocoons. To tackle the problems above, we propose a Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model for the recommender systems. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.