In recent years, knowledge graphs (KGs) have been widely applied in various domains for
different purposes. The semantic model of KGs can represent knowledge through a …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

Collaborative filtering models based on matrix factorization and learned similarities using
Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …

Deep learning techniques are increasingly being applied to solve various machine learning
tasks that use Knowledge Graphs as input data. However, these techniques typically learn a …

Deep Learning and factorization-based collaborative filtering recommendation models have
undoubtedly dominated the scene of recommender systems in recent years. However …

Deep learning classifiers are hugely vulnerable to adversarial examples, and their existence
raised cybersecurity concerns in many tasks with an emphasis on malware detection …

Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …