# How algorithmic popularity bias hinders or promotes quality

arXiv: https://arxiv.org/abs/1707.00574 · HTML: https://mlsanity.com/paper/1707.00574/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2017.

## Built on by (most cited first, 20 of 20)

- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](https://mlsanity.com/paper/2106.06830/) (2021)
- [Embedding Democratic Values into Social Media AIs via Societal Objective Functions](https://mlsanity.com/paper/2307.13912/) (2023)
- [A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions](https://mlsanity.com/paper/2112.05700/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models](https://mlsanity.com/paper/2510.04226/) (2025)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)
- [Modeling Language Usage and Listener Engagement in Podcasts](https://mlsanity.com/paper/2106.06605/) (2021)
- [Recommendation System Simulations: A Discussion of Two Key Challenges](https://mlsanity.com/paper/2109.02475/) (2021)
- [Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation](https://mlsanity.com/paper/2301.07483/) (2023)
- [A survey on bias in machine learning research](https://mlsanity.com/paper/2308.11254/) (2023)
- [Simulating News Recommendation Ecosystem for Fun and Profit](https://mlsanity.com/paper/2305.14103/) (2023)
- [Islander: A Real-Time News Monitoring and Analysis System](https://mlsanity.com/paper/2204.11457/) (2022)
- [A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems](https://mlsanity.com/paper/2301.07639/) (2023)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [Aligning Recommendations with User Popularity Preferences](https://mlsanity.com/paper/2604.01036/) (2026)

## Abstract (arXiv)

Algorithms that favor popular items are used to help us select among many choices, from engaging articles on a social media news feed to songs and books that others have purchased, and from top-raked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, beautiful movies, prestigious information sources, and important discoveries --- in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and ultimately lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content "bubble up" in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the critical trade-off between quality and popularity. We find a regime of intermediate exploration cost where an optimal balance exists, such that choosing what is popular actually promotes high-quality items to the top. Outside of these limits, however, popularity bias is more likely to hinder quality. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.