Recommender Systems as Incentives for Social Learning

Yeon-Koo Che et Johannes Hörner


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.

Codes JEL

  • D82: Asymmetric and Private Information • Mechanism Design
  • D83: Search • Learning • Information and Knowledge • Communication • Belief
  • M52: Compensation and Compensation Methods and Their Effects


Yeon-Koo Che et Johannes Hörner, « Recommender Systems as Incentives for Social Learning », The Quarterly Journal of Economics, vol. 133, n° 2, mai 2018, p. 871–925.

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Publié dans

The Quarterly Journal of Economics, vol. 133, n° 2, mai 2018, p. 871–925