Singular value decomposition model application for e-commerce recommendation system

Abstract

A recommendation system is one of the most important things in today’s technology. It can suggest products that match the user’s preferences. Many fields utilize this system, including e-commerce, using various algorithms. This paper used the matrix factorization-based algorithm, singular value decomposition (SVD), to make a recommendation system based on users’ similarities. Afterward, we implement the model against the ModCloth Amazon dataset. The results imply that the SVD algorithm yields the best accuracy compared to other matrix factorization-based algorithms with root mean square error (RMSE) of 1.055586. Then, we optimized the SVD algorithm by changing the hyperparameters of the algorithm to generate better accuracy and yield a model with an RMSE value of 1.041784.