【公开学术报告】Position Ranking and Auctions for Online Marketplaces

Topic: Position Ranking and Auctions for Online Marketplaces

Date & Time: 9:30-11:00am, Fri. 18^th, January 2019

Venue: Room 2101, Tongji Building A

Language: English

Speaker：Dr. Heng ZHANG (University of Southern California)

GUEST BIO

Heng Zhang is a fifth year Ph.D. candidate at USC Marshall School of Business, Data Sciences and Operations Department. He is interested in Choice Modeling with applications in Revenue Management in a broad sense. Before joining USC, He obtained his M. Sc. in Systems Engineering from University of Pennsylvania, and M. Sc. in Management Science and Engineering and B. Sc. in Applied Mathematics from Beijing University of Posts and Telecommunications. During his Ph.D. studies, he did his internship as a research scientist at Amazon for two summers, where he worked on large-scale machine learning problems.

ABSTRACT

Online e-commerce platforms such as Amazon and Taobao connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers and utilize the top and most salient slots. We present a model that considers consumers' search costs and the externalities sellers impose on each other. This model allows us to study a multi-objective optimization, whose objective includes consumer and seller surplus, as well as the sales revenue, and derive the optimal ranking decision. In addition, we propose the surplus-ordered ranking (SOR) mechanism for selling some of the top slots. This mechanism is motivated in part by Amazon's sponsored search program. We show that our mechanism is near-optimal, performing significantly better than those that do not incentivize the sellers to reveal their private information. Moreover, in practice platforms can provide partial item information in the product list-page to facilitate the consumer search. We generalize our model and demonstrate the robustness of our findings in such environments.