How do you design and implement a recommendation system?

Recommendation systems predict user preferences using collaborative filtering, content-based, or hybrid approaches.\n\n• Collaborative filtering: User-item matrix factorization, neighborhood methods\n• Content-based: Item features similarity, user profile matching\n• Hybrid: Combine multiple approaches, ensemble methods\n• Evaluation: Precision@K, recall@K, NDCG, diversity metrics\n\nExample: E-commerce platform uses matrix factorization for collaborative filtering, product features for content-based recommendations, and hybrid approach for new users. Evaluates with A/B testing measuring click-through rates and conversion metrics.