An Overview of Deep Learning-Based Recommendation Systems and Evaluation Metrics

Pp: 53-71 (19)

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Abstract

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.

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