Title: Balanced News Using Constrained Bandit-based Personalization,
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.