# A Survey on Popularity Bias in Recommender Systems

arXiv: https://arxiv.org/abs/2308.01118 · HTML: https://mlsanity.com/paper/2308.01118/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 181 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 30 of 52)

- [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)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Sequence-Aware Recommender Systems](https://mlsanity.com/paper/1802.08452/) (2018)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [Debiased Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2303.11780/) (2023)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://mlsanity.com/paper/2008.09273/) (2020)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback](https://mlsanity.com/paper/1910.01444/) (2019)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Multi-sided Exposure Bias in Recommendation](https://mlsanity.com/paper/2006.15772/) (2020)
- [Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation](https://mlsanity.com/paper/2109.07946/) (2021)
- [Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation](https://mlsanity.com/paper/2302.04775/) (2023)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems](https://mlsanity.com/paper/2005.01148/) (2020)
- [PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity](https://mlsanity.com/paper/2106.01300/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation](https://mlsanity.com/paper/2006.04275/) (2020)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Personalized News Recommendation with Context Trees](https://mlsanity.com/paper/1303.0665/) (2013)

## Built on by (most cited first, 30 of 43)

- [Large Language Models as Recommender Systems: A Study of Popularity Bias](https://mlsanity.com/paper/2406.01285/) (2024)
- ["Global is Good, Local is Bad?": Understanding Brand Bias in LLMs](https://mlsanity.com/paper/2406.13997/) (2024)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [In-context Ranking Preference Optimization](https://mlsanity.com/paper/2504.15477/) (2025)
- [Unbiased Collaborative Filtering with Fair Sampling](https://mlsanity.com/paper/2502.13840/) (2025)
- [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026)
- [CSRec: Rethinking Sequential Recommendation from A Causal Perspective](https://mlsanity.com/paper/2409.05872/) (2024)
- [Calibrated Recommendations with Contextual Bandits](https://mlsanity.com/paper/2509.05460/) (2025)
- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective](https://mlsanity.com/paper/2505.11318/) (2025)
- [Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring](https://mlsanity.com/paper/2605.12398/) (2026)
- [When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations](https://mlsanity.com/paper/2310.09687/) (2023)
- [Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy](https://mlsanity.com/paper/2509.00109/) (2025)
- [Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2509.25138/) (2025)
- [Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems](https://mlsanity.com/paper/2511.18024/) (2025)
- [Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation](https://mlsanity.com/paper/2511.20673/) (2025)
- [From Personalization to Prejudice: Bias and Discrimination in Memory-Enhanced AI Agents for Recruitment](https://mlsanity.com/paper/2512.16532/) (2025)
- [Bridging Semantic Understanding and Popularity Bias with LLMs](https://mlsanity.com/paper/2601.09478/) (2026)
- [Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit](https://mlsanity.com/paper/2605.27439/) (2026)
- [Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback](https://mlsanity.com/paper/2605.29141/) (2026)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)
- [Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space](https://mlsanity.com/paper/2504.10005/) (2025)
- [Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models](https://mlsanity.com/paper/2504.12898/) (2025)
- [Large Language Models Are Still Misled by Simple Bias Ensembles](https://mlsanity.com/paper/2505.16522/) (2025)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations](https://mlsanity.com/paper/2508.11978/) (2025)
- [Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations](https://mlsanity.com/paper/2508.20945/) (2025)
- [Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation](https://mlsanity.com/paper/2510.09136/) (2025)
- [The Quest for Reliable Metrics of Responsible AI](https://mlsanity.com/paper/2510.26007/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations todays recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss todays literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.