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Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets

2022-10-12 21:53:15
Anton Dereventsov, Anton Bibin
arXiv_AI
       
Abstract

We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, this http URL, 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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.10631

PDF

https://arxiv.org/pdf/2210.10631.pdf

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