Reducing Recommender Systems Biases: An Investigation of Rating Display Designs

54 Pages Posted: 27 Mar 2019 Last revised: 11 Jun 2019

See all articles by Gediminas Adomavicius

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management

Jesse Bockstedt

University of Arizona - Department of Management Information Systems

Shawn Curley

University of Minnesota - Minneapolis

Jingjng Zhang

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

Date Written: February 6, 2019

Abstract

Prior research has shown that online recommendations have a significant influence on consumers’ preference ratings and economic behavior. Specifically, biases induced by observing personalized system recommendations can lead to distortions in users’ self-reported preference ratings after consumption of an item, thus contaminating the users’ subsequent inputs to the recommender system. This, in turn, provides the system with an inaccurate view of user preferences and opens up possibilities of rating manipulation. As recommender systems continue to become increasingly popular in today’s online environments, preventing or reducing such system-induced biases constitutes a highly important and practical research problem. In this paper, we address this problem via the analysis of different rating display designs for the purpose of proactively preventing biases before they occur, i.e., at rating collection time. We use randomized laboratory experimentation to test how the presentation format of personalized recommendations affects the biases generated in post-consumption preference ratings. We demonstrate that graphical rating display designs of recommender systems are more advantageous than numerical designs in reducing the biases, although none are able to remove biases completely. We also show that scale compatibility is a contributing mechanism operating to create these biases, although not the only one. Together, the results have practical implications for the design and implementation of recommender systems as well as theoretical implications for the study of recommendation biases.

Keywords: recommender systems, decision bias, interface design, preference ratings, scale compatibility, experimental research

Suggested Citation

Adomavicius, Gediminas and Bockstedt, Jesse and Curley, Shawn and Zhang, Jingjng, Reducing Recommender Systems Biases: An Investigation of Rating Display Designs (February 6, 2019). Forthcoming, MIS Quarterly; Kelley School of Business Research Paper No. 19-18. Available at SSRN: https://ssrn.com/abstract=3346686

Gediminas Adomavicius

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

19th Avenue South
Minneapolis, MN 55455
United States

Jesse Bockstedt

University of Arizona - Department of Management Information Systems ( email )

AZ
United States

Shawn Curley

University of Minnesota - Minneapolis ( email )

110 Wulling Hall, 86 Pleasant St, S.E.
308 Harvard Street SE
Minneapolis, MN 55455
United States

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

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