Improving Explainable Matrix Factorization with User-Item Features for Recommender Systems

Collaborative Filtering (CF) has been a highly effective approach widely utilized in Recommender Systems, leveraging user-item interactions. Among CF methods, Matrix Factorization (MF) has maintained a prominent status as a state-of-the-art model, delivering personalized item recommendations based on user preferences. Recent advancements have integrated Deep Learning techniques into MF, particularly to capture nonlinear item representations and enhance recommendation accuracy. However, many of these models often overlook user-item features and fail to offer understandable explanations for their recommendations. This absence of explanation mechanisms undermines user trust in recommendations, reducing their relevance and utility. In this study, we introduce E-IUAutoMF, an extension of MF that incorporates explainable user-item based features. This model builds upon the Explainable Matrix Factorization (EMF) framework, utilizing Contractive Autoencoders to extract user and item features and providing explanations using neighborhood explanation techniques. The results of several experiments demonstrate that our proposed model E-IUAutoMF outperforms the other baselines models.