The Impact of Basic Matrix Factorization Refinements on Recommendation Accuracy

Consumers are commonly overloaded with various choices when it comes to the selection of a product or service. Many e-tailers have adopted built-in recommenders to help consumers make more informed decisions. While Accuracy of the recommender agents has high impact on customer satisfaction, achieving high accuracy in these systems is challenging. Various models and techniques were proposed in the literature to improve accuracy of these systems. Matrix factorization (MF) has been widely used in previous studies mostly to overcome cold start problem. In this study, we show that fine-tuning the parameters used in the basic MF model plays a significant role in achieving higher prediction accuracy. Our evaluations are performed on a basic model with and without simple user and item biases on two datasets.