Buyer-Optimal Algorithmic Recommendations

Abstract

In markets where algorithmic data processing is increasingly prevalent, recom-mendation algorithms can substantially affect trade and welfare. We consider a setting in which an algorithm recommends a product based on its value to the buyer and its price. We characterize an algorithm that maximizes the buyer’s expected payoff and show that it strategically biases recommendations to induce lower prices. Revealing the buyer’s value to the seller leaves overall payoffs un-changed while leading to more dispersed prices and a more equitable distribution of surplus across buyer types. These results extend to all Pareto-optimal algorithms and to multiseller markets, with implications for AI assistants and e-commerce ranking systems.

Reference

Shota Ichihashi, and Alex Smolin, “Buyer-Optimal Algorithmic Recommendations”, TSE Working Paper, n. 25-1672, October 2025.