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Recommender systems books focus on the theory and practice of algorithms that personalize content, products, or information for users. They cover foundational methods like collaborative filtering, content-based and hybrid approaches, alongside modern techniques such as matrix factorization, deep learning, sequence models, and bandits. Common themes include handling implicit and explicit feedback, feature engineering, scalable training and serving, evaluation and experimentation (e.g., precision/recall, NDCG, offline validation, A/B testing), and real-world challenges like cold start, sparsity, diversity and serendipity, explainability, fairness, and privacy. Many titles include case studies from e-commerce, streaming, and social platforms, with practical guidance and code examples (often in Python, using tools like Spark or TensorFlow). This category serves students, researchers, and practitioners who design, deploy, and maintain recommendation engines in production.

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