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