Recommender Algorithms in 2026: A Practitioner's Guide: Structured and practical overview of this algorithmic landscape. Math foundations

This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines-from classic neighborhood models to powerful matrix factorization-and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models. A core theme of this guide is the practical integration of the latest technological brea...