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

Authors

  • Qiqi Ding School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Yi Cai School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Ke Xu School of Software Engineering, South China University of Technology, Guangzhou, China
  • Huakui Zhang School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education

Keywords:

Recommender System, Social Network, Curiosity Psychology

Abstract

Curiosity 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.

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Published

2021-05-18

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

Issue

Section

AAAI Student Abstract and Poster Program