Recommender systems play a crucial role in personalized content delivery, with collaborative filtering (CF) being a widely used approach. However, traditional CF methods often struggle to fully capture complex user-item interactions. In this study, we propose neural-stacking models that integrate multiple CF techniques to enhance predictive accuracy. Experimental results show that, among baseline matrix factorization (MF) models, Biased MF and BNMF achieve the best Mean Absolute Error (MAE), demonstrating their effectiveness in modeling user-item relationships. Nonetheless, the proposed neural-stacking models outperform these approaches by dynamically weighting CF models based on contextual factors. Comparisons with deep learning-based CF models (GMF, MLP, and NeuMF) confirm that neural-stacking provides a more personalized and adaptive recommendation strategy. Future research will focus on optimizing model architectures, incorporating additional contextual information, and evaluating scalability for large-scale applications.