Music recommender systems (MRSs) have experienced a boom in recent years, thanks to
the emergence and success of online streaming services, which nowadays make available …

The task of item recommendation requires ranking a large catalogue of items given a
context. Item recommendation algorithms are evaluated using ranking metrics that depend …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

User profiles in collaborative filtering (CF) recommendation technique are built based on
ratings given by users on a set of items. The most eminent shortcoming of the CF technique …

Combining simple elements from the literature, we define a linear model that is geared
toward sparse data, in particular implicit feedback data for recommender systems. We show …

The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …

Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …

Sequential recommendation has been a hot research topic because of its practicability and
high accuracy by capturing the sequential information. As deep learning (DL) based …