Implement collaborative filtering recommendation algorithm
Summary
This MR implements the core collaborative filtering algorithm using SVD for generating personalized content recommendations.
Implementation Details
Algorithm:
- Matrix factorization using Singular Value Decomposition (SVD)
- K-nearest neighbors for similarity matching
- Cosine similarity for item-to-item comparisons
Performance:
-
⚡ Average recommendation time: 45ms (target: <100ms)✅ -
📊 Model accuracy: 76% on test dataset (target: >70%)✅ -
💾 Memory: ~200MB for 100k users -
🔄 Model training: 12 minutes
Caching Strategy:
- Redis for precomputed similarity scores
- 1-hour TTL on cached data
- Reduces latency by 60%
Testing
-
✅ 12 unit tests (100% coverage) -
✅ Integration tests with mock data -
✅ Performance benchmarks included -
✅ CI pipeline configured
Dependencies
- numpy, scikit-learn, redis
- Requires Redis server running
- Python 3.11+
Closes #23 (closed)