Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
This provides a tool for researchers and practitioners in recommendation systems to create realistic simulation environments, though it is incremental as it builds on existing datasets and bandit frameworks.
The paper tackled the problem of generating simulated contextual bandit environments for personalization tasks by using recommendation datasets like MovieLens and IMDb, resulting in environments that reflect real-world user interactions for method development and benchmarking.
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.