Optimal Recommendation Strategies for AI-Powered E-Commerce Platforms: A Study of Duopoly Manufacturers and Market Competition

Chi ZHOU, He LI*, Linlin ZHANG*, Yufei REN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)

Abstract

Artificial intelligence-powered recommendation systems have gained popularity as a tool to enhance user experience and boost sales. Platforms often need to make decisions about which seller to recommend and the strength of the recommendation when conducting recommendations. Therefore, it is necessary to explore the recommendation strategy of the platform in the case of duopoly competition. We develop a game model where two competing manufacturers sell products through an agency contract on a common platform, and they can decide whether or not to provide recommendations to the manufacturers. Our highlight lies in the endogenous recommendation strength of the platform. The findings suggest that it is optimal for the platform to offer recommendation services when the commission rate is high. The platform also prefers to only recommend one manufacturer in the market with low or high competition, but it prefers to recommend both manufacturers in moderately competitive markets. From the view of manufacturers, they can benefit from the recommendation service as long as the commission rate is not too low. Moreover, recommending only one manufacturer consistently yields stronger recommendations compared to recommending multiple manufacturers. However, the impact of recommendation on prices is influenced by the commission rate and product substitutability. These results have significant implications for platform decision making and provide valuable insights into the trade-offs involved in the development of recommendation systems.

Original languageEnglish
Pages (from-to)1086-1106
Number of pages21
JournalJournal of Theoretical and Applied Electronic Commerce Research
Volume18
Issue number2
Early online date7 Jun 2023
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • duopoly competition
  • platform operations
  • pricing strategies
  • product substitutability
  • recommendation strategy

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