As a result of a huge volume of implicit feedback such as browsing and clicks, many
researchers are involving in designing recommender systems (RSs) based on implicit …

The collaborative filtering (CF) based models are capable of grasping the interaction or
correlation of users and items under consideration. However, existing CF-based methods …

Providing useful information to the users by recommending highly demanded products and
services is a fundamental part of the business of many top tier companies. Recommender …

Deep learning-based collaborative filtering methods are studied in recommendation
systems as efficient feature mapping techniques. The aim of these methods is to project the …

In recent years, the ever-growing contents (movies, clothes, books, etc.) accessible and
buyable via the Internet have led to the information overload issue and therefore the item …

This paper provides an innovative deep learning architecture to improve collaborative
filtering results in recommender systems. It exploits the potential of the reliability concept to …

Due to the widespread availability of implicit feedback (eg, clicks and purchases), some
researchers have endeavored to design recommender systems based on implicit feedback …

A recommender system plays a vital role in information filtering and retrieval, and its
application is omnipresent in many domains. There are some drawbacks such as the cold …

Numerous state-of-the-art recommendation frameworks employ deep neural networks in
Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network …

In this paper, a revised recommendation system is constructed that ensembles deep neural
network and matrix factorization under its framework and uses the explicit feedback for …