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Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
Fan Yao · Chuanhao Li · Karthik Abinav Sankararaman · Yiming Liao · Yan Zhu · Qifan Wang · Hongning Wang · Haifeng Xu
Event URL: https://openreview.net/forum?id=k5NCm5mcFT »

The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The rewarding mechanism employed by these platforms creates a competitive environment among creators which affects their production choices and, consequently, content distribution and system welfare. In this work, we uncover a fundamental limit about a class of widely adopted mechanisms, coined Merit-based Monotone Mechanisms, by showing that they inevitably lead to a constant fraction loss of the welfare. To circumvent this limitation, we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition games resulting from BRM possess a potential game structure, which naturally induces the strategic creators' behavior dynamics to optimize any given welfare metric. In addition, the class of BRM can be parameterized so that it allows the platform to directly optimize welfare within the feasible mechanism space even when the welfare metric is not explicitly defined.

Author Information

Fan Yao (University of Virginia)
Chuanhao Li (University of Virginia)
Karthik Abinav Sankararaman (Facebook)
Yiming Liao (Meta)
Yan Zhu (Google)
Qifan Wang (Meta AI)
Hongning Wang (University of Virginia)

I am an associate professor in the Department of Computer Science of University of Virginia. My research interest includes data mining, machine learning, and information retrieval, with a special emphasis on computational user behavior modeling.

Haifeng Xu (University of Chicago)

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