# A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

arXiv: https://arxiv.org/abs/2301.07639 · HTML: https://mlsanity.com/paper/2301.07639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 82 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 30 of 41)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks](https://mlsanity.com/paper/2108.05233/) (2021)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [A Systematic Study of Bias Amplification](https://mlsanity.com/paper/2201.11706/) (2022)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System](https://mlsanity.com/paper/2204.01266/) (2022)
- [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://mlsanity.com/paper/2008.09273/) (2020)
- [Fair Representation Learning for Heterogeneous Information Networks](https://mlsanity.com/paper/2104.08769/) (2021)
- [On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges](https://mlsanity.com/paper/2201.06716/) (2022)
- [How algorithmic popularity bias hinders or promotes quality](https://mlsanity.com/paper/1707.00574/) (2017)
- [A Biased Graph Neural Network Sampler with Near-Optimal Regret](https://mlsanity.com/paper/2103.01089/) (2021)
- [On Structural Explanation of Bias in Graph Neural Networks](https://mlsanity.com/paper/2206.12104/) (2022)
- [A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2107.03415/) (2021)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering](https://mlsanity.com/paper/2204.12326/) (2022)
- [Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation](https://mlsanity.com/paper/2006.04275/) (2020)
- [Exploring Artist Gender Bias in Music Recommendation](https://mlsanity.com/paper/2009.01715/) (2020)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Exposure Inequality in People Recommender Systems: The Long-Term Effects](https://mlsanity.com/paper/2112.08237/) (2021)
- [Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems](https://mlsanity.com/paper/2208.03298/) (2022)

## Abstract (arXiv)

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model performance.