  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.
