Browsing by Author "Jangid, Manisha"

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    Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review
    (2025) Jangid, Manisha
    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.