Lower Bias, Higher Welfare: How Creator Competition Reshapes Bias-Variance Tradeoff in Recommendation Platforms?

TL;DR

The paper addresses how strategic content creators reshape the bias-variance tradeoff in user feature estimation on recommendation platforms by formulating the Content Creator Competition with Bias-Variance Tradeoff ($C^3_{BV}$). It develops a tractable game-theoretic model in which the platform chooses the regularization parameter $\lambda$ and creators respond strategically, showing that $\lambda_{str}^* < \lambda_{non}^*$ for trend-preferring users, while bounds render the effect nuanced for niche-preferring users. Through theoretical analysis in a stylized single-user, two-content-type setting and extensive experiments on synthetic and real-world data (MovieLens-100k and Instant-Video), the authors demonstrate that weaker regularization (lower bias) improves overall user welfare in strategic environments. The findings suggest practical design principles for real-world recommendation systems: accounting for creator competition justifies favoring lower bias in the bias-variance tradeoff to bolster user welfare, especially in trend-dominant user populations.

Abstract

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.

Figures (8)

• Figure 1: Examples for distribution of estimated user embeddings from 200 trials under $\lambda=0$ and $\lambda=1$. The top row shows results for a trend-preferring user; the bottom row for a niche-preferring user. Ground-truth embeddings are marked by 'X', and predictions by blue dots. Additional results with three more $\lambda$ values and an extra sample are provided in \ref{['sec:omitted-figures']}.
• Figure 2: User welfare-$\lambda$ curve for Trend Market and Niche Market data in synthetic experiments.
• Figure 3: User welfare-$\lambda$ curve for experiments based on the MovieLens-100k and Instant-Video datasets. For better visibility, the $y$-axis in Plots (b), (d), (f), and (h) is truncated, as the value at $x = 0$ is significantly smaller and thus omitted.
• Figure 4: Examples for distribution of estimated user embeddings from 200 trials under five different $\lambda$s.
• Figure 5: Iterative dynamics of the LBR algorithm on the synthetic Trend Market dataset with $K=1$ and $K=5$.

Theorems & Definitions (20)

• Definition 3.1: Non-strategic Platform
• Definition 3.2: Strategic Platform
• Definition 4.1: $PreNT$ Game
• Theorem 4.1
• Theorem 4.2
• Remark 4.1
• Proposition 4.3
• Theorem 4.4
• Remark 4.2
• Lemma C.1