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

arXiv: https://arxiv.org/abs/2410.02776 · HTML: https://mlsanity.com/paper/2410.02776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics](https://mlsanity.com/paper/2202.01615/) (2022)
- [Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?](https://mlsanity.com/paper/2110.07131/) (2021)

## Built on by (most cited first, 1 of 1)

- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)

## Abstract (arXiv)

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.