Enhancing Transparency In E-Commerce Recommendations Through Feature-Enhanced Natural Language Explanations (FENLE)

E-commerce recommendation systems often function as opaque "black boxes," limiting user trust and engagement despite their predictive accuracy. This concept paper proposes Feature-Enhanced Natural Language Explanations (FENLE) to address this challenge by combining interpretable product features with dynamic, user-friendly natural-language justifications. A structured methodology is outlined for designing and testing FENLE in experimental settings, with simulated results suggesting significant improvements in user trust, satisfaction, perceived usefulness, and engagement compared to baseline and simple feature-based explanations. While acknowledging trade-offs between accuracy and explainability, the paper emphasizes the value of user-centered design and adaptive explanation strategies. By integrating Explainable Artificial Intelligence (XAI) principles into recommender systems, e- commerce platforms can foster transparency, loyalty, and responsible AI adoption, aligning technical innovation with user expectations and ethical standards.