# Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

arXiv: https://arxiv.org/abs/1901.07555 · HTML: https://mlsanity.com/paper/1901.07555/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

## Built on by (most cited first, 30 of 82)

- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://mlsanity.com/paper/2204.08085/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [Evaluating ChatGPT as a Recommender System: A Rigorous Approach](https://mlsanity.com/paper/2309.03613/) (2023)
- [A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2107.03415/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems](https://mlsanity.com/paper/2203.00376/) (2022)
- [Cross Pairwise Ranking for Unbiased Item Recommendation](https://mlsanity.com/paper/2204.12176/) (2022)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Reenvisioning Collaborative Filtering vs Matrix Factorization](https://mlsanity.com/paper/2107.13472/) (2021)
- [Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation](https://mlsanity.com/paper/2209.11679/) (2022)
- [Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness](https://mlsanity.com/paper/2409.10825/) (2024)
- [Unbiased Collaborative Filtering with Fair Sampling](https://mlsanity.com/paper/2502.13840/) (2025)
- [Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems](https://mlsanity.com/paper/2505.02120/) (2025)
- [Online Recommendations for Agents with Discounted Adaptive Preferences](https://mlsanity.com/paper/2302.06014/) (2023)
- [Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models](https://mlsanity.com/paper/2410.17276/) (2024)
- [Auditing Preferences for Brands and Cultures in LLMs](https://mlsanity.com/paper/2603.18300/) (2026)
- [A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems](https://mlsanity.com/paper/2402.00485/) (2024)
- [Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems](https://mlsanity.com/paper/2406.17475/) (2024)
- [Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings](https://mlsanity.com/paper/2106.07720/) (2021)
- [An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System](https://mlsanity.com/paper/2109.05677/) (2021)

## Abstract (arXiv)

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the `long tail' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.