Theme: Recommendation System Design for Digital Content Platforms

Guest Speaker: Xiao Fu (Fudan University)

Time:  Wednesday, November 27, 2024, 13:00-14:15 PM. 15

Venue: 318, Business School Building

Organizer: Department of Economics

Guest Bio:

  Xiao Fu is an assistant professor in the Department of Applied Economics, School of Management, Fudan University, and a Ph.D. in Economics from the University of Southern California, with research interests in Industrial Economics and Digital Economy, and his research papers have been published in the International Journal of Industrial Organization, Review of Industrial Organization, Economics and Business Administration, and the International Journal of Digital Economics, and the International Journal of Digital Economics. His research papers have been published in International Journal of Industrial Organization, Review of Industrial Organization, Economics Letters, Managerial and Decision Economics, Journal of Management Science and other journals, and he is the chair of the General Project of the National Social Science Foundation of China and the Decision-making Consulting Research of Shanghai Municipality.

Synopsis：

  Digital content platforms implement a variety of recommendation policies to balance the interests of creators and viewers. In this study, we develop a game-theoretic model to examine the determination and impact of recommendation policies within a representative platform. We show that as the creator exerts more effort in content creation, the platform recommends the content to a larger number of viewers. However, the equilibrium level of effort exerted by the creator typically differs from the optimal level of effort for the platform. We identify the conditions under which the creator's effort is either supra-optimal or sub-optimal from the platform's perspective, and the platform's policy remedies are studied.