Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systems

Training reinforcement learning-based recommender systems is often hindered by the lack of dynamic and realistic user interactions. To address this limitation, we introduce Lusifer, a novel environment leveraging Large Language Models (LLMs) to generate simulated user feedback. Lusifer synthesizes user profiles and interaction histories to simulate responses and behaviors toward recommended items, with profiles updated after each rating to reflect evolving user characteristics. Utilizing the MovieLens dataset as a proof of concept, we limited our implementation to the last 40 interactions for each user, representing approximately 39% and 22% of the training sets, to focus on recent user behavior. For consistency and to gain insights into the performance of traditional methods with limited data, we implemented baseline approaches using the same data subset. Our results demonstrate that Lusifer accurately emulates user behavior and preferences, even with reduced training data having an RMSE of 1.3 across various test sets. This paper presents Lusifer's operational pipeline, including prompt generation and iterative user profile updates, and compares its performance against baseline methods. The findings validate Lusifer's ability to produce realistic dynamic feedback and suggest that it offers a scalable and adjustable framework for user simulation in online reinforcement learning recommender systems for future studies, particularly when training data is limited.