  We propose a method for generating simulated contextual bandit environments
for personalization tasks from recommendation datasets like MovieLens, Netflix,
Last.fm, Million Song, etc. This allows for personalization environments to be
developed based on real-life data to reflect the nuanced nature of real-world
user interactions. The obtained environments can be used to develop methods for
solving personalization tasks, algorithm benchmarking, model simulation, and
more. We demonstrate our approach with numerical examples on MovieLens and IMDb
datasets.
