Theory and Toolkits for User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation

Krisztian Balog, Nolwenn Bernard, Saber Zerhoudi, ChengXiang Zhai

Abstract: Interactive AI systems, including search engines, recommender systems, conversational agents, and generative AI applications, are increasingly central to user experiences. However, rigorously evaluating their performance, training them effectively with interaction data, and modeling user behavior for personalization remain significant challenges, often difficult to address reproducibly and at scale. User simulation, which employs intelligent agents to mimic human interaction patterns, offers a powerful and versatile methodology to tackle these interconnected issues. This half-day tutorial provides a comprehensive overview of modern user simulation techniques for interactive AI systems. We will explore the theoretical foundations and practical applications of simulation for system evaluation, algorithm training, and user modeling, emphasizing the crucial connections between these uses. The tutorial covers key simulation methodologies, with a particular focus on recent advancements leveraging large language models, discussing both the opportunities they present and the open challenges they entail. Crucially, we will also provide practical guidance, highlighting relevant toolkits, libraries, and datasets available to researchers and practitioners.

External IDs:doi:10.1145/3726302.3731697