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

AAAI 2021 pp. 15777-15778

doi:10.1609/AAAI.V35I18.17885 /aaai/2021/ding2021aaai-incorporating/

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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Cite

Text

Ding et al. "Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17885

Markdown

[Ding et al. "Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/ding2021aaai-incorporating/) doi:10.1609/AAAI.V35I18.17885

BibTeX

@inproceedings{ding2021aaai-incorporating,
  title     = {{Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)}},
  author    = {Ding, Qiqi and Cai, Yi and Xu, Ke and Zhang, Huakui},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15777-15778},
  doi       = {10.1609/AAAI.V35I18.17885},
  url       = {https://mlanthology.org/aaai/2021/ding2021aaai-incorporating/}
}