A Stacking Ensemble of Collaborative Filtering and Random Forest for Movie Recommendation

Abstract

Recommender systems often struggle to balance global preference patterns, local similarity, and complex user-item interactions within a single model. Existing approaches combine traditional collaborative filtering (CF) methods such as Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) with gradient boosting models such as XGBoost as meta-learners, yet they often fail to capture complex non-linear interactions. In addition, XGBoost may exhibit limitations in stability, as it can become sensitive to training data and show signs of overfitting in certain scenarios. To address this limitation, this study proposes a stacking ensemble-based CF approach that extends a baseline model by incorporating Neural Collaborative Filtering (NCF) as an additional base learner and employing Random Forest (RF) as the meta-learner. These base models capture complementary characteristics: SVD models global latent factors, KNN captures local similarity, and NCF learns non-linear user-item interactions. Experiments are conducted on the MovieLens 100K dataset. The results demonstrate that augmenting the baseline model with NCF and RF reduces prediction ambiguity and improves system convergence stability. The proposed ensemble achieves the lowest RMSE of 0.9113. Although the baseline model with XGBoost slightly outperforms in MAE, the proposed approach achieves higher Recall and F1-score. These results indicate better overall recommendation quality.