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.

翻译：创作者经济彻底改变了个人通过在线平台获利的方式。本文通过将创作者经济建模为用户、平台和内容创作者三方博弈，开创性地研究该领域的在线学习问题——平台在委托代理模型下通过合同与内容创作者互动以激励优质内容生成；同时，平台与用户互动来推荐新内容、获取评价，并最终从内容中获利，这一过程可建模为推荐系统。本研究旨在探索平台如何以在线学习的方式联合优化合同与推荐系统以最大化效用。我们主要分析并比较两类合同：收益分成型合同与特征驱动型合同。收益分成型合同将平台获得的部分奖励支付给内容创作者；而特征驱动型合同则根据内容质量或特征（无论平台获得何种奖励）向内容创作者付费。研究表明，在平滑性假设下，收益分成型合同与推荐策略的联合优化可实现遗憾界 $\Theta(T^{2/3})$。针对特征驱动型合同，我们引入内在维度 $d$ 的概念来刻画合同学习难度，并给出遗憾上界 $\mathcal{O}(T^{(d+1)/(d+2)})$。该上界在线性族情形下是紧的。