Recommender Systems as Incentives for Social Learning

Abstract

This paper studies how a recommender system may incentivize users to learn about a product collaboratively. To improve the incentives for early exploration, the optimal design trades off fully transparent disclosure by selectively overrecommending the product (or “spamming”) to a fraction of users. Under the optimal scheme, the designer spams very little on a product immediately after its release but gradually increases its frequency; and she stops it altogether when she becomes sufficiently pessimistic about the product. The recommender’s product research and intrinsic/naive users “seed” incentives for user exploration and determine the speed and trajectory of social learning. Potential applications for various Internet recommendation platforms and implications for review/ratings inflation are discussed.

JEL codes

Reference

Yeon-Koo Che, and Johannes Hörner, “Recommender Systems as Incentives for Social Learning”, The Quarterly Journal of Economics, vol. 133, n. 2, May 2018, pp. 871–925.