Optimization of hybrid-based Collaborative Filtering using Matrix Factorization, Feedforward Neural Network, and XGBoost - Filimantaptius Gulo; Ronsen Purba; Muhammad Fermi Pasha (The Indonesian Institute of Science and Technology Research, 2026)
Collaborative filtering recommendation systems are widely used in digital applications; however, they still face challenges such as cold-start and first-rater problems, as well as limited accuracy due to their inability to capture complex user–item relationships. This study proposes a hybrid recommendation model that integrates Matrix Factorization, MLP-based Feedforward Neural Network (MLP) and Extreme Gradient Boosting (XGBoost). Experiments were conducted on two real-world datasets, namely MovieLens (movies) and PT XYZ (hotels), to validate the effectiveness of the proposed approach. The results indicate that the hybrid model consistently outperforms baseline methods such as SGD-based Matrix factorization, Matrix factorization +MLP, and user/item-based Collaborative filtering. Specifically, the integration of nonlinear learning through MLP and feature enhancement via XGBoost significantly improves prediction accuracy while mitigating cold-start and first-rater issues. These findings suggest that hybrid machine learning–based approaches can advance the development of more adaptive, accurate, and personalized recommendation systems.