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 a so-called 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. In addition, when the platform sells all slots, we show that our mechanism can be implemented as a Nash equilibrium in a modified generalized second price (GSP) auction.
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