Intermediaries such as Amazon and Google recommend products and services to consumers for which they receive compensation from the recommended sellers. Consumers will find these recommendations useful only if they are informative about the quality of the match between the sellers' offerings and the consumer’s needs. The intermediary would like the consumer to purchase the product from the recommended seller, but is constrained because consumers need not follow the recommendation. I frame the intermediary’s problem as a mechanism design problem in which the mechanism designer cannot directly choose the outcome, but must encourage the consumer to choose the desired outcome. I show that in the optimal mechanism, the recommended seller has the largest non-negative virtual willingness to pay adjusted for the cost of persuasion. The optimal mechanism can be implemented via a handicap auction.

I use this model to provide insights for current policy debates. First, to examine the impact of the intermediary’s use of seller data, I identify types of seller data that lead to benefit or harm to the consumer and sellers. Second, I find that the optimal direct mechanism protects consumer privacy, but consumer data is leaked to sellers under other implementations. Lastly, I show that the welfare-maximizing mechanism increases the consumer surplus, but reduces the joint profit of the intermediary and sellers relative to the revenue-maximizing mechanism. An alternative interpretation of the model as a search engine is discussed.