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 …

Recommender Systems have become a very useful tool for a large variety of domains.
Researchers have been attempting to improve their algorithms in order to issue better …

The MovieLens datasets are widely used in education, research, and industry. They are
downloaded hundreds of thousands of times each year, reflecting their use in popular press …

Matrix completion models are among the most common formulations of recommender
systems. Recent works have showed a boost of performance of these techniques when …

Although recommender systems (RSs) play a crucial role in our society, previous studies
have revealed that the performance of RSs may considerably differ between groups of …

We propose an inductive matrix completion model without using side information. By
factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows …

Low rank matrix completion plays a fundamental role in collaborative filtering applications,
the key idea being that the variables lie in a smaller subspace than the ambient space …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

We develop hierarchical Poisson matrix factorization (HPF), a novel method for providing
users with high quality recommendations based on implicit feedback, such as views, clicks …

Datasets for recommender systems are few and often inadequate for the contextualized
nature of news recommendation. News recommender systems are both time-and location …