Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

Abstract

We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as slows down the system’s learning pace (i.e., the rate of predictive accuracy improvement). We also demonstrate unique benefits of certain hybrid consumption strategies—that is, that take advantage of both popularity- and personalization-based recommendations—in facilitating improvements in consumption relevance over time. Because users’ consumption strategies can significantly influence the longitudinal performance of recommender systems, it is important for designers to analyze the histories of a system’s recommendations and users’ choices to infer and understand users’ consumption strategies. This would enable the system to anticipate users’ consumption behavior and strategically adjust the system’s parameters according to its long-term performance objectives.