Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

With the growing number of Location-Based Social Networks, privacy-preserving point-of-
interest (POI) recommendation has become a critical challenge when helping users discover …

Abstract Web users are flooded with information on the internet, and they feel overwhelmed
by the different choices they have to make online daily. Recommender systems come to their …

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

Adversarial machine learning is the research field investigating vulnerabilities inherent to
machine learning systems' design and ways to defend against them. Recently …

Collaborative filtering models have undoubtedly dominated the scene of recommender
systems in recent years. However, due to the little use of content information, they narrowly …

Federated learning involves training statistical models over edge devices such as mobile
phones such that the training data is kept local. Federated Learning (FL) can serve as an …

Federated learning (FL) trains a global model across a number of decentralized users, each
with a local dataset. Compared to traditional centralized learning, FL does not require direct …

Recently, federated recommendation has become a research hotspot mainly because of
users' awareness of privacy in data. As a recent and important recommendation problem, in …