Many online platforms have adopted a recommender system (RS) to suggest an actual product to the active users according to their preferences. The RS that provides accurate information on users’ past preferences is known as collaborative filtering (CF). One of the most common CF methods is matrix factorization (MF). It is important to note that the MF technique contains several tuned parameters, leading to an expensive and complex black-box optimization problem. An objective function quantifies the quality of a prediction by mapping any possible configuration of hyper-parameters to a numerical score. In this article, we show how a gird search optimization (GSO) can efficiently obtain the optimal value of hyper-parameters an MF and improve the prediction of the collaborative recommender system (CRS). Specifically, we designed a 4 X 4 grid search space, obtained the optimal set of hyper-parameters, and then evaluated the model using these hyperparameters. Furthermore, we evaluated the model using two benchmark datasets and compared it with the stateof-the-art model. We found that the proposed model significantly improves the prediction accuracy, precision@k, and NDCG@k over the state-of-art-the models and handles the sparsity problem of CF.