Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems

Abstract

The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.