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

Information Systems Research, Forthcoming

Kelley School of Business Research Paper No. 19-23

46 Pages Posted: 7 Jun 2019 Last revised: 8 Jul 2019

See all articles by Jingjng Zhang

Jingjng Zhang

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management

Wolfgang Ketter

University of Cologne - Faculty of Management, Economics and Social Sciences; Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management; Erasmus Research Institute of Management (ERIM)

Date Written: May 17, 2019

Abstract

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, user-recommender 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-rises-to-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.

Keywords: dynamics of recommender systems, agent-based modeling, simulation, consumption strategies, prediction accuracy, consumption diversity, consumption relevance

Suggested Citation

Zhang, Jingjng and Adomavicius, Gediminas and Gupta, Alok and Ketter, Wolfgang, Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework (May 17, 2019). Information Systems Research, Forthcoming, Kelley School of Business Research Paper No. 19-23, Available at SSRN: https://ssrn.com/abstract=3392035

Jingjng Zhang (Contact Author)

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

1309 E. Tenth Street
HH4143
Bloomington, IN 47401
United States

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Wolfgang Ketter

University of Cologne - Faculty of Management, Economics and Social Sciences ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

HOME PAGE: http://is3.uni-koeln.de

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

HOME PAGE: http://www.rsm.nl/energy

Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam
Netherlands

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