Online Learning in a Creator Economy

• URL: http://arxiv.org/abs/2305.11381v1
• Date: Fri, 19 May 2023 01:58:13 GMT
• Title: Online Learning in a Creator Economy
• Authors: Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan
• Abstract summary: We study the creator economy as a three-party game between the users, platform, and content creators. We analyze two families of contracts: return-based contracts and feature-based contracts. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret.
• Score: 91.55437924091844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent model through contracts to encourage better content. Additionally, the platform interacts with the users to recommend new content, receive an evaluation, and ultimately profit from the content, which can be modeled as a recommender system. Our study aims to explore how the platform can jointly optimize the contract and recommender system to maximize the utility in an online learning fashion. We primarily analyze and compare two families of contracts: return-based contracts and feature-based contracts. Return-based contracts pay the content creator a fraction of the reward the platform gains. In contrast, feature-based contracts pay the content creator based on the quality or features of the content, regardless of the reward the platform receives. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret $\Theta(T^{2/3})$. For the feature-based contract, we introduce a definition of intrinsic dimension $d$ to characterize the hardness of learning the contract and provide an upper bound on the regret $\mathcal{O}(T^{(d+1)/(d+2)})$. The upper bound is tight for the linear family.

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