This paper studies product ranking mechanisms of a monopolistic online platform in the presence of social learning. The products' quality is initially unknown, but consumers can sequentially learn it as online reviews accumulate. A salient aspect of our problem is that consumers, who want to purchase a product from a list of items displayed by the platform, incur a search cost while scrolling down the list. In this setting, the social learning dynamics, and hence the demand, is aected by the interplay of two unique features: substitution and ranking eects. The platform can in uence the social learning dynamics by adjusting the ranking of the products to ultimately maximize the revenue collected from commission fees for sold items. To formulate the problem in a tractable form, we use a large-market (uid) approximation and show that consumers eventually learn the products' quality and characterize the speed of learning. Armed with this backing, we formulate the platform's ranking problem in the uid setting, where we assume the perspective of an uninformed platform that does not know the true quality vector but rather learns it through consumers' review process. We compare dierent ranking policies based on the worst-case regret with respect to a fullyinformed platform benchmark. Our analysis yields three main insights. First, a greedy policy that maximizes immediate revenue by displaying products based on current ratings may incur highly suboptimal worst-case regret, as it may relegate the most protable products to the lowest positions in the ranking if their current rating is not high enough. Second, a simple variant of the greedy policy can suciently alleviate the regret by balancing the trade-obetween exploration and exploitation. Third, we characterize the critical level of search cost for which the regret does not grow exponentially with the number of products.

Costis Maglaras, M. Scarsini, D. Shin, and S. Vaccari
Journal Article
Publication Date
Operations Research

Full Citation

Maglaras, Costis, M. Scarsini, D. Shin, and S. Vaccari
. “Product Ranking in the Presence of Social Learning.”
Operations Research