8 citations · 24 across the 25 of their papers we have counts for
5 papers · 1 filter
PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
Mohammad Naeimi, Mostafa Haghir Chehreghani
Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias -- the tend…
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Narges Nemati, Mostafa Haghir Chehreghani
Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of us…
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Zahra Akhlaghi, Mostafa Haghir Chehreghani
The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs),…
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation
Nemat Gholinejad, Mostafa Haghir Chehreghani
Graph neural networks (GNNs) have proven to be an effective tool for enhancing the performance of recommender systems. However, these systems often suffer from popularity bias, lea…
Heterophily-Aware Fair Recommendation using Graph Convolutional Networks
Nemat Gholinejad, Mostafa Haghir Chehreghani
In recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only des…