Self-Supervised and Pareto-Optimal Feature Learning for Explainable Customer Segmentation

1. Introduction 1.1 Background and Motivation 1.2 Research Questions 1.3 Structure of the Thesis 2. Theoretical Framework 2.1 Customer Segmentation Concepts 2.2 Explainability in Machine Learning 2.3 Self-Supervised Learning Overview 3. Related Work 3.1 Previous Studies on Feature Learning 3.2 Pareto-Optimality in Machine Learning 3.3 Explainable AI in Customer Segmentation 4. Methodology 4.1 Data Collection and Preprocessing 4.2 Self-Supervised Learning Techniques 4.3 Pareto-Optimal Feature Selection Process 5. Proposed Model 5.1 Model Architecture Design 5.2 Integration of Pareto-Optimality 5.3 Explainability Mechanisms 6. Experiments and Results 6.1 Experimental Setup and Evaluation Metrics 6.2 Comparative Analysis of Algorithms 6.3 Results on Customer Segmentation 7. Discussion 7.1 Interpretation of Findings 7.2 Implications for Business Strategies 7.3 Limitations and Future Research 8. Conclusion 8.1 Summary of Contributions 8.2 Recommendations for Implementation 8.3 Final Thoughts and Conclusions