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arXiv · 1806.09202

Balanced News Using Constrained Bandit-based Personalization

Sayash Kapoor·Vijay Keswani·Nisheeth K. Vishnoi·L. Elisa Celis
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Abstract

We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of de-polarizing 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.

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Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis. 2018-06-24. Balanced News Using Constrained Bandit-based Personalization. https://arxiv.org/abs/1806.09202

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