Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets

TL;DR

This paper introduces a method to create simulated contextual bandit environments from real recommendation datasets, enabling more realistic testing and development of personalization algorithms.

Contribution

It presents a novel approach to generate realistic simulated environments for personalization tasks using existing recommendation datasets.

Findings

Effective simulation of personalization environments from real data

Demonstrated on MovieLens and IMDb datasets

Facilitates benchmarking and development of personalization algorithms

Abstract

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.