Attention-inspired text-based recommender system with explanatory capabilities

Recommender systems are widely used to help users find relevant and personalized items in various domains. However, providing accurate recommendations is not enough to ensure user satisfaction, trust, and engagement. Nowadays, users demand transparency from these systems typically in the form of an explanation of the recommendation given. This paper presents a novel explainable Recommender System designed to generate recommendations from natural language queries while providing model-intrinsic explanations inspired by attention mechanisms. The system adds transparency, interpretability, and new user cold-start capabilities. We evaluate our approach on twelve datasets from diverse domains and languages, demonstrating its effectiveness and robustness. Results show that our proposal achieves competitive accuracy with respect to strong baselines, while consistently outperforming a prior interpretable model developed for the same task.