Enhancing Cold-Start Recommendations with Innovative Co-SVD: A Sparsity Reduction Approach

Abstract

This study introduces a novel methodology to improve recommendation systems, specifically targeting the challenging cold-start problem. By creatively combining Collaborative Singular Value Decomposition (Co-SVD) with an innovative sparsity reduction approach, our study significantly improves the accuracy of the recommendation and mitigates the challenges posed by sparse user-item interaction matrices. We conducted a comprehensive set of experiments, using a sample e-Commerce data set, to demonstrate the effectiveness of our approach. The results illustrate the superiority of our enhanced Co-SVD model over traditional Co-SVD, content-based filtering, and random recommendation in various evaluation metrics. In particular, our methodology excels in cold-start scenarios, providing accurate recommendations for users with limited interaction history. The implications of our research extend to practical applications in e-marketing, user engagement, and personalized marketing strategies, highlighting the potential for enhanced customer satisfaction and business success. This work represents a critical step forward in the evolution of recommendation systems and underscores the importance of addressing the cold-start problem in modern online services.