SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems

TL;DR

This paper introduces SUBER, a synthetic RL environment using large language models to simulate human behavior, facilitating the training and evaluation of recommender systems without extensive real user data.

Contribution

It presents a novel, modular framework leveraging LLMs to simulate human interactions, addressing data scarcity and evaluation challenges in RL-based recommender systems.

Findings

Effective training of RL recommenders using synthetic human behavior.

Demonstrated improvements in movie and book recommendation tasks.

Open-source software for the RL environment is publicly available.

Abstract

Reinforcement learning (RL) has gained popularity in the realm of recommender systems due to its ability to optimize long-term rewards and guide users in discovering relevant content. However, the successful implementation of RL in recommender systems is challenging because of several factors, including the limited availability of online data for training on-policy methods. This scarcity requires expensive human interaction for online model training. Furthermore, the development of effective evaluation frameworks that accurately reflect the quality of models remains a fundamental challenge in recommender systems. To address these challenges, we propose a comprehensive framework for synthetic environments that simulate human behavior by harnessing the capabilities of large language models (LLMs). We complement our framework with in-depth ablation studies and demonstrate its effectiveness with experiments on movie and book recommendations. Using LLMs as synthetic users, this work introduces a modular and novel framework to train RL-based recommender systems. The software, including the RL environment, is publicly available on GitHub.