<![CDATA[The rapid growth of streaming platforms triggers information overload, necessitating effective recommendation systems. However, conventional Collaborative Filtering faces extreme data sparsity, reaching 98.47% in the MovieLens dataset, and operates as a black-box lacking transparent predictive rationalization. This study aims to develop a robust movie recommendation system that accurately predicts sparse data while offering novelty through predictive transparency using Explainable AI (XAI). The methodology applies Top-N Filtering to densify the matrix, followed by optimizing Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) algorithms via GridSearchCV. Results demonstrate that matrix densification to 81.20% successfully accelerated algorithmic performance. SVD significantly outperformed KNN, not only in error reduction (RMSE 0.182090) but also in real-world relevance metrics, achieving a Precision@10 of 0.917, Recall@10 of 0.806, and F1-Score@10 of 0.858. The main contribution of this research lies in demystifying the SVD black-box using XAI. Individual-level evaluation using Local Interpretable Model-Agnostic Explanations (LIME) quantitatively proved that factorization model recommendations are driven more by universal trend sentiments (global mean contribution +0.729) than personal history matching. Furthermore, spatial exploration through Principal Component Analysis (PCA) highlighted the complexity of SVD's 100-dimensional latent space that cannot be linearly reduced, confirming the mathematical need for caution in 2D visual interpretations. In conclusion, the synergy of optimized SVD and the XAI framework produces a robust recommendation system that breaks black-box limitations, directly impacting the enhancement of transparency and user trust in digital platforms.]]>
