Addressing the Cold Start Problem of Recommendation Systems: A Comprehensive Review of Traditional and Emerging Solutions

Recommendation systems are crucial for managing the overwhelming volume of information available due to the rapid growth of the internet. Providing personalized recommendations is vital for enhancing user experience and is a key driver for the growth of a business through improved user retention, which often leads to increased sales. Among the various approaches for building a recommendation system, Collaborative Filtering is widely adopted and is regarded as the most successful approach. It excels in leveraging the available historical user-item interactions in the system to find similarities that can be utilized for recommendations.However, the cold start problem—where new users and items lack sufficient data severely hampers the effectiveness of such systems. This paper reviews how emerging approaches such as Generative Adversarial Networks, Associative Rule Mining, Data Imputation, and Active Learning address this challenge. The paper further elucidates each approach’s strengths, limitations, and practical implications, accompanied by an analysis of their impact on real-world applications.Through the actionable insights outlined to overcome the identified gaps in the existing solutions, this paper contributes to advancing the research on addressing the cold start problem, paving the path for future researchers to develop more dynamic and scalable recommendation systems.