The essential of improving user experience and engagement in e-commerce platform is the building of a recommendation system. In this study, implementation and comparison of several matrix factorization models for an e-commerce platform is presented. For this study, dataset was taken from an online marketing platform, which was stored in a MongoDB database. Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), Alternating Least Squares (ALS), and Neural Network Matrix Factorization models were implemented and tested with the dataset. Performance of the models were evaluated by using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The model of neural network matrix factorization produced the lowest RMSE (0.002) and MAE (0.001). Consequently, it could potentially be recommended as the appropriate model. By delivering personalised recommendations to individual user preferences, this study aims to improve user engagement and satisfaction of the e-commerce platform.