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Online Learning in a Creator Economy
Zhu, Banghua Karimireddy, Sai Praneeth Jiao, Jiantao Jordan, Michael
Zhu, Banghua
Karimireddy, Sai Praneeth
Jiao, Jiantao
Jordan, Michael
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Machine Learning
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Journal article
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http://creativecommons.org/licenses/by/4.0/
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English
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Abstract
The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms. In this paper, we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users, a platform, and content creators. The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system. We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting. We analyze return-based and feature-based contracts. Under smoothness assumptions, return-based contracts achieve regret Θ(T2/3). For feature-based contracts, we introduce an intrinsic dimension d and prove a regret bound O(T(d+1)/(d+2)), which is tight for linear families.
Citation
B. Zhu, S.P. Karimireddy, J. Jiao, M. Jordan, "Online Learning in a Creator Economy," Artificial Intelligence Science and Engineering, vol. 2, no. 1, pp. 36-48, 2026, https://doi.org/10.23919/aise.2026.000003.
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Artificial Intelligence Science and Engineering
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IEEE