Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks
By providing personalized suggestions to users, recommender systems have become
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
Robust collaborative filtering to popularity distribution shift
In leading collaborative filtering (CF) models, representations of users and items are prone
to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are …
to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are …
Adaptive popularity debiasing aggregator for graph collaborative filtering
The graph neural network-based collaborative filtering (CF) models user-item interactions as
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …
A comparative analysis of bias amplification in graph neural network approaches for recommender systems
Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …
recommendations and help them overcome the problem of information overload. Currently …
Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
Mitigating Exposure Bias in Recommender Systems–A Comparative Analysis of Discrete Choice Models
When implicit feedback recommender systems expose users to items, they influence the
users' choices and, consequently, their own future recommendations. This effect is known as …
users' choices and, consequently, their own future recommendations. This effect is known as …
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from
financial fraud detection to fake news detection. However, current GAD methods largely …
financial fraud detection to fake news detection. However, current GAD methods largely …