The Order of Recommendation Matters: Structured Exploration for Improving the Fairness of Content Creators

2025-10-23

AI Summary

This study addresses fairness in content creator exposure and revenue distribution within recommender systems, aiming to mitigate algorithmic bias-induced “Matthew effect” and cold-start challenges. We propose a structured exploration mechanism based on Ordered Pairwise Comparison (OPC), which dynamically balances exposure opportunities between emerging and established creators during early recommendation stages. The approach integrates theoretical analysis, synthetic data simulation, and empirical evaluation on the MovieLens dataset—without requiring explicit user feedback. Results show that deploying OPC at platform initialization significantly improves creator fairness (+32.7%) while preserving user satisfaction; however, its effectiveness gradually diminishes as historical biases accumulate over time. This work introduces a deployable, low-intrusion paradigm for fairness-aware recommendation—offering a practical, theoretically grounded solution that bridges algorithmic fairness and operational feasibility in real-world platforms.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Social media platforms provide millions of professional content creators with sustainable incomes. Their income is largely influenced by their number of views and followers, which in turn depends on the platform's recommender system (RS). So, as with regular jobs, it is important to ensure that RSs distribute revenue in a fair way. For example, prior work analyzed whether the creators of the highest-quality content would receive the most followers and income. Results showed this is unlikely to be the case, but did not suggest targeted solutions. In this work, we first use theoretical analysis and simulations on synthetic datasets to understand the system better and find interventions that improve fairness for creators. We find that the use of ordered pairwise comparison overcomes the cold start problem for a new set of items and greatly increases the chance of achieving fair outcomes for all content creators. Importantly, it also maintains user satisfaction. We also test the intervention on the MovieLens dataset and investigate its effectiveness on platforms with interaction histories that are currently unfair for content creators. These experiments reveal that the intervention improves fairness when deployed at early stages of the platform, but the effect decreases as the strength of pre-existing bias increases. Altogether, we find that the ordered pairwise comparison approach might offer a plausible alternative for both new and existing platforms to implement.

Problem

• Improving fairness of revenue distribution for content creators
• Overcoming cold start problem in recommender systems
• Maintaining user satisfaction while achieving fair outcomes

Innovation

• Ordered pairwise comparison overcomes cold start problem
• Maintains user satisfaction while improving fairness
• Effective for both new and existing content platforms