Improving Personalized Recommendations via the Use of Ridge Regularization in Matrix Factorization

Abstract

For many online sites, personalized recommendation algorithms are now crucial to increasing user engagement and happiness.
These systems rely on Matrix Factorization (MF) algorithms as their main approach for efficient recommendation creation and
user-item interaction modeling. It is still difficult to guarantee that MF models are resilient and can generalize, especially when
dealing with sparse and noisy datasets. Here, we zero in on how to optimize MF for individualized suggestions by using L2 regularization
methods. To reduce the risk of overfitting, L2 regularization uses a penalty term that is equal to the squared Frobenius
norm of the item and user matrices. This feature encourages the learning of latent representations that are more stable and
generic. Our goal is to find out how L2 regularization affects recommendation performance and show that it helps MF-based
recommendation systems be more accurate and resilient. We use extensive trials on real-world datasets to assess the efficacy of
L2-regularized MF models in comparison to baseline methods. L2 regularization has the ability to optimize MF for customized
recommendations across several application domains, as our findings show that it greatly improves recommendation accuracy
and generalization performance.