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Recommender Systems (RSs) are used to provide users with personalized itemrecommendations and help them overcome the problem of information overload.Currently, recommendation methods based on deep learning are gaining groundover traditional methods such as matrix factorization due to their ability torepresent the complex relationships between users and items and to incorporateadditional information. The fact that these data have a graph structure and thegreater capability of Graph Neural Networks (GNNs) to learn from thesestructures has led to their successful incorporation into recommender systems.However, the bias amplification issue needs to be investigated while usingthese algorithms. Bias results in unfair decisions, which can negatively affectthe company reputation and financial status due to societal disappointment andenvironmental harm. In this paper, we aim to comprehensively study this problemthrough a literature review and an analysis of the behavior against biases ofdifferent GNN-based algorithms compared to state-of-the-art methods. We alsointend to explore appropriate solutions to tackle this issue with the leastpossible impact on the model performance.

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