Popularity Bias in Online Dating Platforms: Theory and Empirical Evidence

48 Pages Posted: 10 Mar 2022 Last revised: 17 Sep 2023

See all articles by Musa Celdir

Musa Celdir

Carnegie Mellon University - David A. Tepper School of Business

Soo-Haeng Cho

Carnegie Mellon University - Tepper School of Business

Elina H. Hwang

University of Washington - Michael G. Foster School of Business

Date Written: July 15, 2023

Abstract

Problem Definition: Generating recommendations of compatible dating partners is a challenging task for online dating platforms because uncovering users' idiosyncratic preferences is difficult. Thus, platforms tend to recommend popular users to others more frequently than unpopular users. This paper studies such popularity bias in an online dating platform's recommendations and its consequences for users' likelihood of finding dating partners.

Methodology/Results: Motivated by the empirical evidence that a user's chance of being recommended by the platform's algorithm increases significantly with the user's popularity, we study an online dating platform’s incentive that generates popularity bias by modeling the platform's recommendations and users' subsequent interactions in a three-stage matching game. Our analysis shows that the recommendations that maximize the platform's revenue and those that maximize the number of successful matches between users are not necessarily at odds, even though the former leads to a higher bias against unpopular users. Unbiased recommendations result in significantly lower revenue for the platform and fewer matches when users' implicit cost of evaluating incoming messages is low. Popular users help the platform generate more revenue and a higher number of successful matches as long as these popular users do not become ``out of reach." We validate our theoretical results by running simulations of the platform based on a machine-learning-based predictive model that estimates users' behavior.

Managerial Implications: Our result indicates that an online dating platform can increase revenue and users' chances of finding dating partners simultaneously with a certain degree of bias against unpopular users. Online dating platforms can use our theoretical results to understand user behavior and our predictive model to improve their recommendation systems (e.g., by selecting a set of users leading to the highest probabilities of matching or other revenue-generating interactions).

Keywords: Algorithmic Bias, Game Theory, Machine Learning, Matching, Platform

Suggested Citation

Celdir, Musa and Cho, Soo-Haeng and Hwang, Elina H., Popularity Bias in Online Dating Platforms: Theory and Empirical Evidence (July 15, 2023). Available at SSRN: https://ssrn.com/abstract=4053204 or http://dx.doi.org/10.2139/ssrn.4053204

Musa Celdir

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Soo-Haeng Cho (Contact Author)

Carnegie Mellon University - Tepper School of Business ( email )

Pittsburgh, PA 15213-3890
United States

Elina H. Hwang

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

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