.class_implementation_cf_recommender.txt

[Reference: See .index.txt for complete file listing]

Collaborative Filtering Recommender Implementation Instructions
=========================================================

Class: CFRecommender
-------------------

Purpose:
Implement collaborative filtering for clause recommendations.

Implementation Details:

1. Matrix Factorization
---------------------
- User-clause matrix
- Implicit feedback handling
- Regularization techniques

2. Training Process
-----------------
- Alternating least squares
- Hyperparameter optimization
- Cold start handling

3. Recommendation Generation
--------------------------
- Similarity computation
- Ranking algorithms
- Diversity promotion

Code Structure:
```python
class CFRecommender:
    def __init__(self, num_factors=100, regularization=0.01):
        self.num_factors = num_factors
        self.regularization = regularization
        self.user_factors = None
        self.item_factors = None
        
    def build_user_matrix(self, interactions):
        """Build user-clause interaction matrix"""
        pass
        
    def train_collaborative_filter(self, train_data):
        """Train CF model"""
        pass
        
    def get_recommendations(self, user_id, n=10):
        """Get clause recommendations"""
        pass
        
    def update_preferences(self, user_id, clause_id, rating):
        """Update user preferences"""
        pass
```

Key Considerations:
- Scalability
- Cold start problems
- Update frequency
- Performance metrics