This paper investigates the application of dimensionality reduction techniques, specifically Singular Value Decomposition (SVD), to improve the performance of recommender systems. Recommender systems are widely used in e-commerce to provide personalized product recommendations based on customer preferences. The paper addresses the challenges of producing high-quality recommendations, handling large volumes of customer and product data, and achieving high coverage despite data sparsity. Collaborative filtering, a successful approach in recommender systems, is noted for its effectiveness but faces limitations in scalability and sparsity. The authors conduct two experiments to evaluate the effectiveness of SVD in reducing the dimensionality of recommender system databases. The first experiment compares the quality of recommendations generated by SVD with those generated by collaborative filtering using explicit ratings from a movie dataset. The second experiment compares the effectiveness of both methods in producing Top-N lists based on real-life customer purchase data from an e-commerce site. The results suggest that SVD can potentially meet the challenges of recommender systems under certain conditions, particularly in handling sparse data and improving online performance. The paper concludes that SVD may be a promising technology for enhancing the scalability and efficiency of recommender systems, although further research is needed to understand its limitations and optimal applications.This paper investigates the application of dimensionality reduction techniques, specifically Singular Value Decomposition (SVD), to improve the performance of recommender systems. Recommender systems are widely used in e-commerce to provide personalized product recommendations based on customer preferences. The paper addresses the challenges of producing high-quality recommendations, handling large volumes of customer and product data, and achieving high coverage despite data sparsity. Collaborative filtering, a successful approach in recommender systems, is noted for its effectiveness but faces limitations in scalability and sparsity. The authors conduct two experiments to evaluate the effectiveness of SVD in reducing the dimensionality of recommender system databases. The first experiment compares the quality of recommendations generated by SVD with those generated by collaborative filtering using explicit ratings from a movie dataset. The second experiment compares the effectiveness of both methods in producing Top-N lists based on real-life customer purchase data from an e-commerce site. The results suggest that SVD can potentially meet the challenges of recommender systems under certain conditions, particularly in handling sparse data and improving online performance. The paper concludes that SVD may be a promising technology for enhancing the scalability and efficiency of recommender systems, although further research is needed to understand its limitations and optimal applications.