RECOSIM: A Universal, Accurate, and Scalable Simulation Framework for Online Community Recommendations

As recommender systems become increasingly important components in online communities, studying their impact on these communities becomes ever more crucial. Facing the high costs and ethical risks of real-world social experiments, researchers construct recommendation simulators to study the interactions between recommender systems and users. However, existing simulators face challenges in providing universal, accurate, and scalable interaction modeling for various types of online communities involving millions of contents and users with diverse action types. To address these challenges, we propose RECOSIM, a simulation framework capable of offering efficient recommendation interaction simulations across a wide range of scenarios. RECOSIM decomposes the user agent into five fundamental modules: Encode Model, Decode Model, Activity Model, Scoring Model, and Generation Model, allowing for accurate and extensible modeling of user behavior and interaction dynamics. The recommender system agent adheres to established industry architectures, implementing three stages and four fundamental strategies, thereby improving generalizability across various platforms and the computational efficiency of simulation. Utilizing two real-world datasets (Weibo and Zhihu), we validate the accuracy and stability of each component and the overall framework of RECOSIM, demonstrating the reliability of RECOSIM as a simulation environment. Subsequently, we delve into analyzing the impact of the four fundamental recommendation strategies on online communities, providing design inspirations for enhancing user engagement and community growth.