Explainable recommender system directed by reconstructed explanatory factors and multi-modal matrix factorization

Matrix factorization (MF)-based recommender systems (RSs) as black-box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi-modal matrix factorization (ERS-REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi-modal matrix, ERS-REFMMF utilizes the Funk-singular value decomposition method at the foundational layer to decompose the multi-modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi-objective optimization algorithms. ERS-REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many-objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.