<![CDATA[Recommendation systems (RS) are vital tools for mitigating information overload and data sparsity challenges in modern e-commerce platforms. This study focuses on developing and evaluating a Collaborative Filtering (CF) model utilizing Singular Value Decomposition (SVD) as a Matrix Factorization technique, applied to the publicly available E-commerce dataset. The dataset, comprising nine interconnected transactional tables, presents significant data sparsity due to limited explicit user ratings relative to the vast product catalog. The SVD model was implemented to decompose the highly sparse User-Item interaction matrix into lower-rank latent factor matrices, thereby capturing underlying purchasing patterns and user preferences. The model's performance was rigorously validated using k-fold cross-validation and assessed via standard accuracy metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The experimental results demonstrated high predictive accuracy, achieving an RMSE of 1.25 and an MAE of 0.98. These findings indicate that the SVD model effectively overcomes the sparsity challenge inherent in large-scale e-commerce transactional data, providing robust prediction capabilities that surpass established industry benchmarks (e.g., RMSE » 1.31, MAE » 1.04 found in similar studies). The successful implementation validates SVD as a highly effective approach for generating personalized, high-quality product recommendations, offering substantial business implications for enhancing customer engagement and maximizing Average Order Value (AOV)]]>
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