Unleashing the Power of SVD and Louvain Community Detection for Enhanced Recommendations

Recommendation systems play a vital role in delivering personalized content to users, thereby enhancing their overall experiences across diverse applications. Collaborative filtering based recommendation systems have demonstrated success through the application of matrix factorization techniques. However, the incessant growth in dataset size and complexity presents challenges regarding the scalability of recommendation algorithms. Consequently, addressing these scalability concerns becomes imperative to ensure the seamless functioning of recommendation systems in handling increasingly large and diverse datasets. This research introduces an innovative method that seamlessly integrates matrix factorization techniques and community detection algorithms to effectively tackle the scalability issue in recommendation systems. Through numerous experiments utilizing real-world datasets, the proposed method's efficiency is thoroughly assessed. These compelling findings underscore the method's potential as a promising solution for constructing robust and scalable recommendation systems effectively. Ultimately, the overarching objective is to enhance user experiences by providing personalized and relevant content recommendations that cater to the evolving needs of modern recommendation systems. By optimizing scalability and recommendation accuracy, this innovative approach seeks to elevate the efficacy and user satisfaction of recommendation systems across various domains.