{"title":"Popularity Bias in Online Dating Platforms: Theory and Empirical Evidence","authors":"Musa Eren Celdir, Soo-Haeng Cho, Elina H. Hwang","doi":"10.1287/msom.2022.0132","DOIUrl":null,"url":null,"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). Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0132 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":4.8000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"M&som-Manufacturing & Service Operations Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1287/msom.2022.0132","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
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). Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0132 .
期刊介绍：
M&SOM is the INFORMS journal for operations management. The purpose of the journal is to publish high-impact manuscripts that report relevant research on important problems in operations management (OM). The field of OM is the study of the innovative or traditional processes for the design, procurement, production, delivery, and recovery of goods and services. OM research entails the control, planning, design, and improvement of these processes. This research can be prescriptive, descriptive, or predictive; however, the intent of the research is ultimately to develop some form of enduring knowledge that can lead to more efficient or effective processes for the creation and delivery of goods and services.
M&SOM encourages a variety of methodological approaches to OM research; papers may be theoretical or empirical, analytical or computational, and may be based on a range of established research disciplines. M&SOM encourages contributions in OM across the full spectrum of decision making: strategic, tactical, and operational. Furthermore, the journal supports research that examines pertinent issues at the interfaces between OM and other functional areas.
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