| The exponential growth of digital media has led to significant information overload, making it necessary the creation of robust recommendation systems (RS) that personalize content search and assist users in decision-making. Although traditional collaborative filtering (CF) models provide robust, computationally efficient baselines, and Deep Learning (DL) architectures perform very well at capturing complex, non-linear interactions, the challenge of the optimization of predictive accuracy still remains open. To study and bridge the gap between linear baselines and sophisticated neural representations, this study conducts a comprehensive evaluation of traditional CF algorithms (SVD, NMF, k-NN, Co-Clustering) alongside a custom TensorFlow-based Neural Collaborative Filtering model (DLRecom), through a unified Ensemble Learning framework. Based on the MovieLens 1M benchmark dataset, our purpose is to reduce model variance and mitigate overfitting, with predictive performance being evaluated rigorously using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experimental results reveal that, through bagging, all algorithmic paradigm’s predictive accuracy is enhanced, with the bagged SVD ensemble pointed out as the optimal configuration, achieving a test MAE of 0.6841 and an RMSE of 0.8666, thus outperforming the best single-model baseline. Furthermore, this research provides a reproducible experimental protocol, highlighting how ensemble techniques deliver robust performance gains over isolated recommendation algorithmic architectures. |
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