Paper Title
A Comparative Review of the Deep Learning based Recommender Systems

Abstract
Given the ever-increasing volume of online data, recommender systems have been a viable procedure to overcome such data over-burden. The utilize of recommender systems cannot be exaggerated due to its broad choice in numerous programs and its potential effect on progressing numerous issues with over-selection. Therefore, in later a long time, Deep Learning has been considered by many natural language processing researchers. The impact of Deep Learning is additionally widespread, and its adequacy in retrieving information and researching the recommender systems has recently been demonstrated. Deep Learning could be a department of machine learning and algorithms that have a high capacity to classify information due to their progressive structure. Of course, Deep learning in recommender systems is thriving. The purpose of this article is to provide an overview of the recommender systems based on Deep Learning. While reviewing different articles in different years, we have acquainted ourselves with different applications and algorithms. Keywords - Deep Learning (DL), Recommender Systems (RS), Hybrid Recommender Systems (HRS), Collaborative Filtering (CF), Content-based filter