Balanced News Using Constrained Bandit-Based Personalization

Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis

IJCAI 2018 pp. 5835-5837

doi:10.24963/IJCAI.2018/854 /ijcai/2018/kapoor2018ijcai-balanced/

Abstract

We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of depolarizing content and allowing users to escape their filter bubble. The balancing is done according to flexible user-defined constraints, and leverages recent advances in constrained bandit optimization. We showcase our balanced news feed by displaying it side-by-side with the news feed produced by a traditional (polarized) feed.

PDF IJCAI Semantic Scholar

Cite

Text

Kapoor et al. "Balanced News Using Constrained Bandit-Based Personalization." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/854

Markdown

[Kapoor et al. "Balanced News Using Constrained Bandit-Based Personalization." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/kapoor2018ijcai-balanced/) doi:10.24963/IJCAI.2018/854

BibTeX

@inproceedings{kapoor2018ijcai-balanced,
  title     = {{Balanced News Using Constrained Bandit-Based Personalization}},
  author    = {Kapoor, Sayash and Keswani, Vijay and Vishnoi, Nisheeth K. and Celis, L. Elisa},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {5835-5837},
  doi       = {10.24963/IJCAI.2018/854},
  url       = {https://mlanthology.org/ijcai/2018/kapoor2018ijcai-balanced/}
}