A factorization based recommender system for online services

Abstract

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an NMF model should be selected in a recommender system setting. We first present a general system architecture in which any kind of factorization model can be used. Then, in order to see how accurate the NMF model fits the data, we randomly erase some parts of a real data set that is gathered from an online food ordering service, and we reconstruct the erased parts by using the NMF model. We report the mean squared errors for different parameter settings and different divergences.