Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review
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Date
2025
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Abstract
Recommendation systems (RS) have become prevalent across different domains includ
ing 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 exten
sively studied topic has shown significant potential in addressing these challenges in RS.
Deep learning captures intricate patterns of interaction and precisely reflects user prefer
ences, 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 under
standing of the core concepts and terminology of recommendation systems and signifi
cance 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 pertain
ing 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.