Recommender systems as mechanisms for social learning

Believe it or not, ChatGPT dug this up for me among the ocean of economic literature!

Recommender Systems as Mechanisms for Social Learning

QJE 2017 | Yeon-Koo Che , Johannes Hörner. https://doi.org/10.1093/qje/qjx044

This article 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; 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.

I will write a summary and thought later—hopefully, soon.