# Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

arXiv: https://arxiv.org/abs/2203.00376 · HTML: https://mlsanity.com/paper/2203.00376/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems](https://mlsanity.com/paper/1912.06933/) (2019)
- [Recommending Items in Social Tagging Systems Using Tag and Time Information](https://mlsanity.com/paper/1406.7727/) (2014)
- [Support the Underground: Characteristics of Beyond-Mainstream Music Listeners](https://mlsanity.com/paper/2102.12188/) (2021)
- [Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics](https://mlsanity.com/paper/1501.07716/) (2015)

## Built on by (most cited first, 8 of 8)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems](https://mlsanity.com/paper/2301.07639/) (2023)
- [A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations](https://mlsanity.com/paper/2303.00400/) (2023)
- [Private Selection with Heterogeneous Sensitivities](https://mlsanity.com/paper/2501.05309/) (2025)
- [Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach](https://mlsanity.com/paper/2510.21728/) (2025)

## Abstract (arXiv)

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.