MFExplain: An Interactive Tool for Explaining Movie Recommendations Generated with Matrix Factorization

Abstract

Recommender systems have become integral in guiding users through the overwhelming abundance of online content. As these systems assume an ever-increasing role in shaping user decisions and preferences, there is a growing demand for clarity in their decision-making processes to instill trust. Recommendation algorithms with a high degree of accuracy such as matrix factorization are highly regarded and widely adopted. Nonetheless, these algorithms tend to exhibit high complexity in their logic and architecture, rendering them challenging to explain to end-users. This issue has been recognized and many tools have presented possible solutions. Many of the implemented approaches, however, have demonstrated shortcomings due to disregarding some user-centered properties or overly concentrating on unraveling the underlying algorithmic intricacy. This work presents MFExplain, an innovative tool for explaining movie recommendations generated with matrix factorization. The tool aims to explain recommendations by relying on the provision of intuitive justifications. Leveraging interactivity and cutting-edge dimensionality reduction techniques enables the tool to also encourage exploration, allow user feedback, and foster many desirable recommender system properties that enrich the user experience.