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<p>With the rapid growth of digital content on the web, search engines and recommendation systems (RS) have become important tools to search and discover meaningful information efficiently. Traditional RSs often struggle to generate accurate and personalized recommendations due to the overwhelming expansion of online data, leading to information overload. Therefore, a better understanding of modern deep learning-based solutions is required to overcome these limitations. Deep learning techniques have emerged as a powerful method, utilizing their ability to model complex, nonlinear relationships and extract meaningful patterns from multidimensional datasets. Recent advancements such as Graph Neural Networks (GNNs), Transformers, and Reinforcement learning have further improved recommendation accuracy by capturing intricate user–item interactions and long-range dependencies. Consequently, deep learning-based RSs have gained popularity due to their ability of modeling user preferences, behaviors, and item characteristics at multiple levels, enhancing accuracy, personalization, and scalability. The objective of this review is to systematically explore these developments and provide an understanding of current trends. This review systematically studies various state-of-the-art deep learning-based RSs, focusing on their architectures, implementation progress, and practical applications. We analyze their effectiveness in extracting intrinsic user and item features, examine their strengths and limitations, and highlight emerging trends in the field. The primary contribution is to compare traditional and deep learning-based recommendation approaches with their key challenges and future research opportunities. Additionally, we discuss fundamental issues related to traditional and deep learning-based RS, which continue to hinder the widespread adoption of deep learning techniques in RSs. Overall, this survey presents a structured framework to guide future research and support the development of more robust, scalable, and efficient deep learning methodologies for RSs.</p>

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Deep learning-based recommendation systems: a comprehensive review of methods and applications

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Ishwari Singh Rajput,
  • Anand Shanker Tewari,
  • Arvind Kumar Tiwari

摘要

With the rapid growth of digital content on the web, search engines and recommendation systems (RS) have become important tools to search and discover meaningful information efficiently. Traditional RSs often struggle to generate accurate and personalized recommendations due to the overwhelming expansion of online data, leading to information overload. Therefore, a better understanding of modern deep learning-based solutions is required to overcome these limitations. Deep learning techniques have emerged as a powerful method, utilizing their ability to model complex, nonlinear relationships and extract meaningful patterns from multidimensional datasets. Recent advancements such as Graph Neural Networks (GNNs), Transformers, and Reinforcement learning have further improved recommendation accuracy by capturing intricate user–item interactions and long-range dependencies. Consequently, deep learning-based RSs have gained popularity due to their ability of modeling user preferences, behaviors, and item characteristics at multiple levels, enhancing accuracy, personalization, and scalability. The objective of this review is to systematically explore these developments and provide an understanding of current trends. This review systematically studies various state-of-the-art deep learning-based RSs, focusing on their architectures, implementation progress, and practical applications. We analyze their effectiveness in extracting intrinsic user and item features, examine their strengths and limitations, and highlight emerging trends in the field. The primary contribution is to compare traditional and deep learning-based recommendation approaches with their key challenges and future research opportunities. Additionally, we discuss fundamental issues related to traditional and deep learning-based RS, which continue to hinder the widespread adoption of deep learning techniques in RSs. Overall, this survey presents a structured framework to guide future research and support the development of more robust, scalable, and efficient deep learning methodologies for RSs.