1. Introduction

2. User and Item Similarity-Based Methods

3. Hybrid and Combined Approaches

4. Deep Learning

5. Algorithmic and Mathematical Modelling Methods

6. Evaluation Methodology

7. Challenges and Limitations of Current Methods for Big Data in Recommendation Systems

8. Real-World Applications and Case Studies of the Methods for Big Data in Recommendation Systems

9. Future Directions for Big Data in Recommendation Systems: Improvement, Ethics, and Bias Perspectives

10. Conclusion