We develop a general agent-based modeling and computational simulation approach to study the impact of various factors on the temporal dynamics of recommender systems’ performance. The proposed agent-based simulation approach allows for comprehensive analysis of longitudinal recommender systems performance under a variety of diverse conditions, which typically is not feasible with live real-world systems. We specifically focus on exploring the product consumption strategies and show that, over time, userrecommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on the system’s recommendations to make item choices generally tends to make the recommender system less useful in the long run or, more specifically, negatively impacts the longitudinal dynamics of several important dimensions of recommendation performance. Furthermore, we explore the nuances of the performance paradox via additional explorations of longitudinal dynamics of recommender systems for a variety of user populations, consumption strategies, as well as personalized and non-personalized recommendation approaches. One interesting discovery from our exploration is that a certain hybrid consumption strategy, i.e., where users rely on a combination of both personalized- and popularity-based recommendations, offers a unique ability to substantially improve consumption relevance over time. In other words, for such hybrid consumption settings, recommendation algorithms facilitate the general “quality-risesto-the-top” phenomenon, which is not present in the pure popularity-based consumption. In addition to discussing a number of interesting performance patterns, the paper also analyzes and provides insights into the underlying factors that drive such patterns. Our findings have significant implications for the design and implementation of recommender systems.

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Keywords dynamics of recommender systems, agent-based modeling, simulation, consumption strategies, prediction accuracy, consumption diversity, consumption relevance.
Persistent URL hdl.handle.net/1765/119001
Journal Information Systems Research
Citation
Zhang, Jingjing, Adomavicius, G., Gupta, A, & Ketter, W. (2019). Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework. Information Systems Research. Retrieved from http://hdl.handle.net/1765/119001