Understanding the bias-variance tradeoff in user representation learning is essential for improving recommendation quality in modern content platforms. While well studied in static settings, this tradeoff becomes significantly more complex when content creators strategically adapt to platform incentives. To analyze how such competition reshapes the tradeoff for maximizing user welfare, we introduce the Content Creator Competition with Bias-Variance Tradeoff framework, a tractable game-theoretic model that captures the platform's decision on regularization strength in user feature estimation. We derive and compare the platform's optimal policy under two key settings: a non-strategic baseline with fixed content and a strategic environment where creators compete in response to the platform's algorithmic design. Our theoretical analysis in a stylized model shows that, compared to the non-strategic environment, content creator competition shifts the platform's optimal policy toward weaker regularization, thereby favoring lower bias in the bias-variance tradeoff. To validate and assess the robustness of these insights beyond the stylized setting, we conduct extensive experiments on both synthetic and real-world benchmark datasets. The empirical results consistently support our theoretical conclusion: in strategic environments, reducing bias leads to higher user welfare. These findings offer practical implications for the design of real-world recommendation algorithms in the presence of content creator competition.

翻译：理解用户表征学习中的偏差-方差权衡对于提升现代内容平台的推荐质量至关重要。尽管在静态环境中已有深入研究，但当内容创作者根据平台激励机制进行策略性调整时，这一权衡关系会变得显著复杂。为分析此类竞争如何重塑以最大化用户福利为目标的权衡关系，我们提出了"含偏差-方差权衡的内容创作者竞争"框架——一个可处理的博弈论模型，该模型捕捉了平台在用户特征估计中对正则化强度的决策。我们推导并比较了两种关键设定下平台的最优策略：固定内容的非策略性基线环境，以及创作者根据平台算法设计展开竞争的策略性环境。在一个典型化模型中的理论分析表明，与非策略性环境相比，内容创作者竞争促使平台的最优策略向更弱正则化方向偏移，从而在偏差-方差权衡中更倾向于降低偏差。为验证这些洞见在典型化设定之外的稳健性，我们在合成数据集和真实世界基准数据集上进行了大量实验。实证结果一致支持我们的理论结论：在策略性环境中，降低偏差能带来更高的用户福利。这些发现为存在内容创作者竞争的现实推荐算法设计提供了实践启示。