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

Jingjing Zhang (), Gediminas Adomavicius (), Alok Gupta () and Wolfgang Ketter ()
Additional contact information
Jingjing Zhang: Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405;
Gediminas Adomavicius: Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
Alok Gupta: Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455;
Wolfgang Ketter: Department of Management, Economics, and Social Sciences, University of Cologne, 50923 Cologne, Germany; and Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University Rotterdam, 3062 PA Rotterdam, Netherlands

Information Systems Research, 2020, vol. 31, issue 1, 76-101

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 and consumption strategies, as well as personalized and nonpersonalized recommendation approaches. One interesting discovery from our exploration is that a certain hybrid consumption strategy—that is, 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 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1287/isre.2019.0876 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:31:y:2020:i:1:p:76-101

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().