Sustainable transparency on recommender systems: Bayesian ranking of images for explainability

Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco

Abstract: Highlights•A new model named BRIE is proposed for visual-based recommendation explainability.•Existing models use highly optimized architectures but suboptimal training policies.•BRIE uses a more adequate learning objective and use of training data.•BRIE outperforms performance of state-of-the-art models in benchmark datasets.•BRIE can reduce model size by up to 64 times and carbon emissions by up to 75%.