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Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review

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Abstract

Recommendation systems (RS) have become prevalent across different domains including music, e-commerce, e-learning, entertainment, and social media to address the issue of information overload. While traditional RS approaches have achieved significant success in delivering recommendations, they still face issues including sparse data, diversity, cold start, and long tail problem. The emergence of deep learning as a prominent and extensively studied topic has shown significant potential in addressing these challenges in RS. Deep learning captures intricate patterns of interaction and precisely reflects user preferences, allowing for encoding complex abstractions in data representation and enhancing information processing capabilities. This paper provides an extensive survey of the existing literature on recommendation systems. We will begin by providing a foundational understanding of the core concepts and terminology of recommendation systems and significance of deep learning. Secondly, we talk about the original studies being conducted on deep learning methods and solutions to address “Cold start and long tail” challenges in recommendation. Thirdly, we examine the potential future directions of research pertaining to deep learning-based recommender systems (DLRS). Our review provides valuable insights for both researchers and practitioners in using deep learning to address challenges in recommendation and in developing effective and efficient recommendation systems.

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  1. Department of Computer Science & Engineering, Central University of Haryana, Haryana, India

    Manisha Jangid & Rakesh Kumar

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  1. Manisha Jangid
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  2. Rakesh Kumar
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Ms. Manisha Jangid and Dr. Rakesh Kumar have equally contributed in this research paper.

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Jangid, M., Kumar, R. Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review. Multimed Tools Appl 84, 2293–2325 (2025). https://doi.org/10.1007/s11042-024-20262-3

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