In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …

Learning and development, or L&D, plays an important role in talent management, which
aims to improve the knowledge and capabilities of employees through a variety of …

Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …

Session-based recommendation systems (SBRs) aim to capture user preferences over time
by taking into account the sequential order of interactions within sessions. One promising …

Fairness-aware recommendation eliminates discrimination issues to build trustworthy
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …

Graph collaborative filtering (GCF) has gained considerable attention in recommendation
systems by leveraging graph learning techniques to enhance collaborative filtering (CF) …

Graph collaborative filtering (GCF) has emerged as a prominent method in recommendation
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …

Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …

In the field of information system (IS) security, a comprehensive enumeration of potential
threats remains a formidable challenge. This study introduces a novel methodology …