Optimizing Matrix Factorization for Personalized Recommendations Using Ridge Regularization

Personalized recommendation systems have become indispensable tools for enhancing user engagement and satisfaction in various online platforms. Matrix Factorization (MF) algorithms serve as fundamental techniques in these systems, allowing for the efficient modeling of user-item interactions and the generation of tailored recommendations. However, ensuring the robustness and generalization capability of MF models remains a challenge, particularly in the presence of sparse and noisy datasets. In this study, we focus on optimizing MF for personalized recommendations through the incorporation of L2 regularization techniques. By introducing a penalty term based on the squared Frobenius norm of the user and item matrices, L2 regularization promotes the learning of more stable and generalized latent representations, thereby mitigating overfitting. We aim to investigate the impact of L2 regularization on recommendation performance and to demonstrate its effectiveness in improving the accuracy and robustness of MF-based recommendation systems. We conduct comprehensive experiments on real-world datasets, evaluating the performance of L2-regularized MF models against baseline approaches. Our results indicate that L2 regularization significantly enhances recommendation accuracy and generalization performance, highlighting its potential to optimize MF for personalized recommendations in diverse application domains