Recommendation Engine

This is a solid foundation for building product or content recommenders with collaborative filtering, matrix factorization (SVD), and hybrid approaches. You get practical code for handling the usual pain points: cold start problems with fallback chains, data sparsity using dimensionality reduction instead of naive similarity, and popularity bias through diversity re-ranking. The evaluation section covers precision@k, recall@k, and NDCG for measuring recommendation quality. What I like is the realistic approach to production issues, like always filtering already-interacted items and implementing graceful degradation when personalization fails. If you're building a recommendation system from scratch or fixing one that only shows bestsellers, this gives you the patterns that actually matter.