Deep Learning-Based Recommendation System: Systematic Review and Classification

• Published in: IEEE access Vol. 11; pp. 113790 - 113835
• Main Authors: Li, Caiwen, Ishak, Iskandar, Ibrahim, Hamidah, Zolkepli, Maslina, Sidi, Fatimah, Li, Caili
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Classification algorithms; Customer satisfaction; Deep learning; Distance learning; Electronic commerce; Electronic learning; Graph neural networks; Machine learning; Measurement; Neural networks; recommendation system; Recommender systems; Recurrent neural networks; Social networks; state-of-the-art techniques; Surveys; Systematic review; Systematics; term classification
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3323353

In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature regarding classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains. To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems.