The ever-increasing information on the internet and the rapid development
of online movies, songs, and stores have enhanced the demands of customers to obtain
the required information within the least time. A Recommendation System (RS) is
designed to help customers with personalized information and interests to avoid
overloading issues in entertainment and social media. Though traditional methods have
made noteworthy developments, RS encounters challenges such as data limitations and
cold starts. The present study aims to review the developments in the field of deep
learning-based RS, thereby providing the required information for researchers. In
addition, several applicable domains of employing deep learning-based RS have been
analyzed. The review has been organized into RS type, deep learning approaches, deep
learning-based recommendation systems in various applications, and evaluation
metrics.
Keywords: Content-based approach, Collaborative filtering, Deep Learning, Discriminative approach, Generative approach, Recommendation systems.
