A Semantically Aware Explainable Recommender System using Asymmetric Matrix Factorization

摘要

Matrix factorization is an accurate collaborative filtering method for predicting user preferences. However, it is a black box system that lacks transparency, providing little information about both users and items in comparison with white box systems. White box systems can easily generate explanations, relying on the rich information foundation that these systems exploit in an explicit manner. However, the accuracy of recommendations is generally low. In this work, we take advantage of the Semantic Web in the process of building a black box model which can make recommendations that can be explained. Our experiments show that our proposed method succeeds in producing lower error rates and in explaining its outputs.