Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …

Training datasets fundamentally impact the performance of machine learning (ML) systems.
Any biases introduced during training (implicit or explicit) are often reflected in the system's …

In recent years, Automated Machine Learning (AutoML) has become increasingly important
in Computer Science due to the valuable potential it offers. This is testified by the high …

The demand for ubiquitous information processing over the Web has called for the
development of context-aware recommender systems capable of dealing with the problems …

Recommender systems have recently been singled out as a fascinating area of research,
owing to the technological progress in mobile devices, such as smartphones and tablets, as …

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural
heritage area, which takes into account the activities on social media performed by the target …

In recent years, AutoML is booming as the time-consuming and iterative tasks involved in
developing a machine learning model can be automated using AutoML. It aims to lessen the …

As digitalization is gradually transforming reality into Big Data, Web search engines and
recommender systems are fundamental user experience interfaces to make the generated …

The existing content-based recommendation methods have two major limitations. First, due
to the defects of the items and the user model matching algorithms, the recommendation …