A hybrid recommendation system combines multiple recommendation approaches to reduce the weaknesses of any single method.
Combination Methods
- Weighted: combine scores from different models
- Switching: choose a method based on context
- Mixed: present recommendations from different methods
- Feature combination: use collaborative features inside content-based models
- Cascade: refine recommendations through multiple stages
- Meta-level: use the output of one model as input to another
Example
Netflix-style systems can combine collaborative patterns, content metadata, context, and deep learning models.
Why It Helps
Content-Based Filtering can help with new items. Collaborative Filtering can discover items that content features alone would miss. Combining them can improve robustness and cold-start handling.