Online Learning in a Creator Economy

TL;DR

This paper models the creator economy as a three-party game involving users, creators, and platforms, proposing online learning strategies to optimize contracts and recommendation systems for maximizing platform utility.

Contribution

It introduces a joint optimization framework for contracts and recommender systems in the creator economy, analyzing regret bounds for different contract types.

Findings

Joint optimization achieves regret $ heta(T^{2/3})$ for return-based contracts.

Feature-based contracts' regret depends on intrinsic dimension $d$, with an upper bound of $O(T^{(d+1)/(d+2)})$.

Upper bounds are tight for linear contract families.

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.