Strategic Recommendation Algorithms: Overselling and Demarketing Information Designs

We analyze recommendation algorithms that firms can engineer to strategically provide information to consumers about products with uncertain matches. Monopolists who cannot alter prices can design recommendation algorithms to oversell the product instead of algorithmically recommending perfectly matching products. However, when prices are endogenous or when competition is rampant, firms opt to lower their persuasive claims and instead choose to fully reveal the product's match (i.e., maximize recall and precision). As competition strengthens, the algorithms will shift to demarket their products in order to soften competition. When a platform designs a recommendation algorithm for products sold by third party sellers we find that overselling is not an equilibrium strategy of the platform, but demarketing might be. Overselling entails designing an algorithm that recommends badly fitting products to consumers, which would lower the consumers' ex-ante willingness to pay, and thus increase competition among the sellers and lower the platform's profit. Demarketing, in contrast, softens the competition among sellers from the information perspective, which can be lucrative for the platform.