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.

本文探讨创作者经济中的在线学习，通过将创作者经济建模为用户、平台和内容创作者之间的三方博弈，分析并比较基于回报和基于特征两类契约，同时优化推荐策略，以期最大化效用。