Why Matrix Factorization Works Well in Recommender Systems: A Systems-Based Explanation

Authors

Griselda Acosta, The University of Texas at El PasoFollow
Manuel Hernandez, The University of Texas at El PasoFollow
Natalia Villanueva-Rosales, The University of Texas at El PasoFollow
Eric Smith, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

7-2019

Comments

Technical Report: UTEP-CS-19-67

Abstract

Many computer-based services use recommender systems that predict our preferences based on our degree of satisfaction with the past selections. One of the most efficient techniques making recommender systems successful is matrix factorization. While this technique works well, until now, there was no general explanation of why it works. In this paper, we provide such an explanation.