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Optimal Search Segmentation Mechanisms for Online Platform Markets

  • Zhenzhe ZhengEmail author
  • R. Srikant
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11920)

Abstract

Online platforms, such as Airbnb, hotels.com, Amazon, Uber and Lyft, can control and optimize many aspects of product search to improve the efficiency of marketplaces. Here we focus on a common model, called the discriminatory control model, where the platform chooses to display a subset of sellers who sell products at prices determined by the market and a buyer is interested in buying a single product from one of the sellers. Under the commonly-used model for single product selection by a buyer, called the multinomial logit model, and the Bertrand game model for competition among sellers, we show the following result: to maximize social welfare, the optimal strategy for the platform is to display all products; however, to maximize revenue, the optimal strategy is to only display a subset of the products whose qualities are above a certain threshold. This threshold depends on the quality of all products, and can be computed in linear time in the number of products.

Keywords

Online platform markets Bertrand competition game Search segmentation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Coordinated Science LabUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Coordinated Science Lab, Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignChampaignUSA

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