Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?

• Main Author: Strömqvist, Zakris
• Format: Others
• Language: English
• Published: Uppsala universitet, Statistiska institutionen 2018
• Subjects: Recommender systems; Collaborative filtering; Matrix factorization; Probability Theory and Statistics; Sannolikhetsteori och statistik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653

One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then made sparse in two different ways to simulate different kinds of data. The accuracy of MF is then measured on each of the simulated sparse matrices. This shows that the matrix factorization models are sensitive to the degree of information available. For high levels of sparsity the MF performs badly but as the information level increases the accuracy of the models improve, for both samples.