Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation

TL;DR

This paper introduces a novel method to improve exposure fairness for long-tail content publishers in recommender systems by repurposing existing components, validated through large-scale online experiments.

Contribution

It proposes a new approach to enhance fairness for long-tail publishers by repurposing existing recommender system components, maintaining quality and fairness simultaneously.

Findings

Improved exposure fairness for long-tail publishers.

Maintained high recommendation quality.

Validated effectiveness through large-scale online AB experiments.

Abstract

Recommender systems play a crucial role in shaping information we encounter online, whether on social media or when using content platforms, thereby influencing our beliefs, choices, and behaviours. Many recent works address the issue of fairness in recommender systems, typically focusing on topics like ensuring equal access to information and opportunities for all individual users or user groups, promoting diverse content to avoid filter bubbles and echo chambers, enhancing transparency and explainability, and adhering to ethical and sustainable practices. In this work, we aim to achieve a more equitable distribution of exposure among publishers on an online content platform, with a particular focus on those who produce high quality, long-tail content that may be unfairly disadvantaged. We propose a novel approach of repurposing existing components of an industrial recommender system to deliver valuable exposure to underrepresented publishers while maintaining high recommendation quality. To demonstrate the efficiency of our proposal, we conduct large-scale online AB experiments, report results indicating desired outcomes and share several insights from long-term application of the approach in the production setting.