Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)
Keywords:Recommender System, Social Network, Curiosity Psychology
AbstractCuriosity affects users' selections of items, and it motivates them to explore the items regardless of their interests. This phenomenon is particularly common in social networks. However, the existing social-based recommendation methods neglect such feature in social network, 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 problem, we propose a novel 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.
How to Cite
Ding, Q., Cai, Y., Xu, K., & Zhang, H. (2021). Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15777-15778. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17885
AAAI Student Abstract and Poster Program