Explainability in AI-powered shopping recommendations: its impact on user trust, verification behavior, and feature adoption

1. Introduction 2. Background and Context 2.1 Evolution of AI in Retail 2.2 Importance of Explainability in AI 2.3 User Trust and Verification Behavior 3. Explainability in AI-powered Recommendations 3.1 Definitions and Concepts 3.2 Types of Explainable AI Approaches 3.3 Challenges in Implementing Explainability 4. Impact on User Trust 4.1 Trust Building through Transparency 4.2 Case Studies and Examples 4.3 Measuring User Trust in AI 5. Verification Behavior of Users 5.1 Understanding Verification Behavior 5.2 Influence of Explainability on Verification 5.3 Studies of User Interaction 6. Feature Adoption in Shopping Platforms 6.1 Role of Explainability in Feature Adoption 6.2 User Experience Design Considerations 6.3 Metrics for Measuring Adoption 7. Case Studies and Practical Applications 7.1 Successful Implementations 7.2 Lessons Learned from Retailers 7.3 Future Potential and Opportunities 8. Conclusion and Future Research 8.1 Summary of Findings 8.2 Limitations of Current Research 8.3 Directions for Future Studies