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ML-based recommendation system using collaborative and content-based filtering
Create a sophisticated recommendation engine that combines collaborative filtering, content-based filtering, and hybrid approaches to provide personalized recommendations.
Collaborative filtering
Content-based filtering
Hybrid recommendations
Real-time updates
A/B testing
Performance metrics
Recommendation algorithms
Matrix factorization
Python ML libraries
API development
Performance optimization
Build recommendation systems
Implement filtering algorithms
Handle sparse matrices
Optimize recommendations
Evaluate recommenders