Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation
success is challenged by the diversity of user preferences, structural sparsity of user-item …

This survey paper provides a comprehensive analysis of the evolution and current
landscape of recommendation systems, extensively used across various web applications. It …

The recommendation ecosystem involves interactions between recommender systems
(Computer) and users (Human). Orthogonal to the perspective of recommender systems, we …

This survey provides an exhaustive exploration of the evolution and current state of
recommendation systems, which have seen widespread integration in various web …

High-dimensional and incomplete (HDI) data commonly arise in various Big Data-related
applications, eg, recommender systems and bioinformatics. Representation is a learning …

The cold start problem has always been a major challenge for recommender systems. It
arises when the system lacks rating records for new users or items. Addressing the …

Recommender systems (RS) are increasingly leveraging the power of graphs to enhance
accuracy. However, we stipulate that existing methods don't take into consideration the …

In the recent world with the increasing trend of online activities, there is a rapid growth of
online users and online services resulting in high-dimensional sparse user–item interaction …

The deep neural network (DNN)-based recommender system (RS) has drawn much
attention recently and provided state-of-the-art results. Although many DNN-based RSs …

Activation functions are essential in deep learning, and the rectified linear unit (ReLU) is the
most widely used activation function to solve the vanishing gradient problem. However …