Recommender systems are algorithms designed to predict user preferences and suggest relevant items from large catalogs—whether products, movies, music, or content. They power personalized experiences across e-commerce, streaming platforms, social media, and search engines by addressing the information overload problem: helping users discover what they need without manual searching. The field balances three fundamental challenges: accuracy (relevance), diversity (avoiding filter bubbles), and scalability (handling millions of users and items in real-time). Understanding both classic techniques like collaborative filtering and modern deep learning approaches is essential, as most production systems combine multiple methods in hybrid architectures to leverage their complementary strengths.