Abstract Summary
The increasing complexity of information access systems necessitates innovative evaluation methods beyond real-world user data, which is often biased, incomplete, or difficult to obtain due to privacy concerns. Simulation and synthetic data methods provide controlled and reproducible environments for training, evaluating, refining, and benchmarking algorithms in information retrieval and personalization. This workshop aims to bring together researchers and practitioners to share insights, discuss best practices, and address pressing open problems by exploring synergies in the use of synthetic data and simulation for Information Retrieval (IR). Building on prior work in the community, we welcome methodological advances, ethical considerations, and practical applications of these approaches. The workshop also aims to produce a report summarizing key discussions, challenges, and future directions, serving as a reference point for researchers and practitioners working at the intersection of simulation, synthetic data, and IR.
