An Improved Collaborative User Product Recommendation System Using Computational Intelligence with Association Rules

In order to address the issue of information overload, recommendation systems have grown in popularity in recent years. They do this by presenting users with the most relevant products from a vast quantity of data. Online collaborative product recommendations aim to help people find their favorite products by identifying exactly identical neighbors between persons or products based on their shared ratings in the past. However, with the rapidly growing number of items and users, neighbor selection becomes more challenging due to the scant data. This research proposes a hybrid model-based product recommendation system that divides transformed user space using the improved Apriori algorithms. In order to densely classify products, it uses the principle component analysis data reduction technique, which may also lessen the computational complexity of intelligent product recommendations. When compared to the current methods, the experiment findings show that the suggested strategy may produce more dependable and customized product recommendations in addition to offering excellent accuracy performance.