Recommender Systems as Mechanisms for Social Learning

IF 11.1 1区 经济学 Q1 ECONOMICS Quarterly Journal of Economics Pub Date : 2018-05-01 DOI:10.1093/QJE/QJX044
Yeon-Koo Che, Johannes Hörner
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引用次数: 95

Abstract

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.
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推荐系统作为社会学习机制
本文研究了推荐系统如何激励用户协同学习产品。为了提高早期探索的动机,最佳设计通过选择性地向一小部分用户过度推荐产品(或“垃圾邮件”)来换取完全透明的披露。在最优方案下,设计师在产品发布后立即对其进行垃圾邮件处理,但频率逐渐增加;当她对产品变得足够悲观时,她就完全停止了。推荐人的产品研究和内在/天真用户为用户探索“种子”激励,并决定社交学习的速度和轨迹。讨论了各种互联网推荐平台的潜在应用以及对审查/评级膨胀的影响。
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来源期刊
CiteScore
24.20
自引率
2.20%
发文量
42
期刊介绍: The Quarterly Journal of Economics stands as the oldest professional journal of economics in the English language. Published under the editorial guidance of Harvard University's Department of Economics, it comprehensively covers all aspects of the field. Esteemed by professional and academic economists as well as students worldwide, QJE holds unparalleled value in the economic discourse.
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