Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Interpretability has recently gained attention in the field of machine learning, for it is crucial
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …

Algorithmic fairness and explainability are foundational elements for achieving responsible
AI. In this paper, we focus on their interplay, a research area that is recently receiving …

Advancements in mathematical programming have made it possible to efficiently tackle
large-scale real-world problems that were deemed intractable just a few decades ago …

Fairness in machine learning (ML) has received much attention. However, existing studies
have mainly focused on the distributive fairness of ML models. The other dimension of …

This study employed knowledge discovery in databases (KDD) to extract and discover
knowledge from the Benue State Polytechnic (Benpoly) admission database and used a …

Recent studies have highlighted significant fairness issues in Graph Transformer (GT)
models, particularly against subgroups defined by sensitive features. Additionally, GTs are …