Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

A protuberant issue of the present time is that, organizations from different domains are
struggling to obtain effective solutions for detecting online-based fake news. It is quite …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative
filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …