Recommendation systems play a vital role in helping consumers by suggesting products that align with their preferences. These systems leverage advanced algorithms, many of which are rooted in machine learning techniques. However, selecting the most suitable algorithm for a recommendation system can be challenging due to the numerous options available. Additionally, developing these systems presents various challenges, with the “cold start” problem being particularly significant. This problem arises when there is insufficient data in the product dataset, making it difficult to provide accurate product recommendations to new users. The cold start issue occurs because there is no existing user purchase history or item ratings, making it hard for the system to predict user preferences.