# Reducing Popularity Influence by Addressing Position Bias

arXiv: https://arxiv.org/abs/2412.08780 · HTML: https://mlsanity.com/paper/2412.08780/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [Correcting for Selection Bias in Learning-to-rank Systems](https://mlsanity.com/paper/2001.11358/) (2020)
- [Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?](https://mlsanity.com/paper/2206.12858/) (2022)

## Built on by (most cited first, 2 of 2)

- [RewardRank: Optimizing True Learning-to-Rank Utility](https://mlsanity.com/paper/2508.14180/) (2025)
- [Towards Position-Robust Talent Recommendation via Large Language Models](https://mlsanity.com/paper/2604.02200/) (2026)

## Abstract (arXiv)

Position bias poses a persistent challenge in recommender systems, with much of the existing research focusing on refining ranking relevance and driving user engagement. However, in practical applications, the mitigation of position bias does not always result in detectable short-term improvements in ranking relevance. This paper provides an alternative, practically useful view of what position bias reduction methods can achieve. It demonstrates that position debiasing can spread visibility and interactions more evenly across the assortment, effectively reducing a skew in the popularity of items induced by the position bias through a feedback loop. We offer an explanation of how position bias affects item popularity. This includes an illustrative model of the item popularity histogram and the effect of the position bias on its skewness. Through offline and online experiments on our large-scale e-commerce platform, we show that position debiasing can significantly improve assortment utilization, without any degradation in user engagement or financial metrics. This makes the ranking fairer and helps attract more partners or content providers, benefiting the customers and the business in the long term.