seniorRecommendation Systems
What is explainability in recommendation systems?
Updated May 16, 2026
Short answer
Explainability refers to understanding why a recommendation was made.
Deep explanation
Explainable recommendation systems provide reasons behind suggestions, improving trust and transparency. Techniques include feature attribution, rule-based explanations, and attention visualization.
Real-world example
Amazon showing 'Customers who bought this also bought...'
Common mistakes
- Using black-box models without interpretability.
Follow-up questions
- Why explainability matters?
- What is SHAP in recommendations?