Che, Yeon-Koo and Hörner, Johannes (2018) Recommender Systems as Incentives for Social Learning. The Quarterly Journal of Economics, 133 (2). pp. 871-925.

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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.

Item Type: Article
Language: English
Date: May 2018
Refereed: Yes
JEL Classification: D82 - Asymmetric and Private Information
D83 - Search; Learning; Information and Knowledge; Communication; Belief
M52 - Compensation and Compensation Methods and Their Effects (stock options, fringe benefits, incentives, family support programs, seniority issues)
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 18 May 2018 10:19
Last Modified: 02 Apr 2021 15:57
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