Sistem Rekomendasi Film dengan Collaborative Filtering Berbasis SVD, KNN dan Explainable AI pada Dataset MovieLens

Abstract

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.