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Bibliographic Details
Main Author:	Indo-continental Academic Publishers
Format:	Recurso digital
Language:	English
Published:	Zenodo 2026
Online Access:	https://doi.org/10.5281/zenodo.19099903
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Table of Contents:

The rapid growth of digital platforms has fundamentally transformed how information, products, and services are discovered and consumed. In this data-driven era, recommender systems have emerged as a cornerstone of modern intelligent applications, powering personalized experiences across domains such as e-commerce, entertainment, healthcare, education, and travel. From suggesting movies and products to optimizing large-scale decision-making processes, recommender systems play a pivotal role in shaping user engagement and satisfaction. The book “Recommender Systems Engineering: Algorithms, Scalable Architectures, and Industrial Use Cases” is conceived as a comprehensive resource that bridges the gap between theoretical foundations and real-world deployment of recommendation technologies. While classical texts often emphasize algorithms in isolation, this volume brings together both algorithmic depth and engineering practicality, addressing the challenges of building scalable, robust, and production-ready recommender systems. This multi-author volume brings together contributions from academicians, researchers, and industry practitioners with diverse expertise in machine learning, data engineering, distributed systems, and software architecture. Each chapter reflects a unique perspective, combining foundational concepts with hands-on insights drawn from real-world applications. The collaborative nature of this book ensures a balanced treatment of theory, implementation strategies, and emerging trends. The book is structured to guide readers through the complete lifecycle of recommender system development. It begins with the fundamentals of recommendation techniques, including content-based filtering, collaborative filtering, and hybrid approaches. It then progresses to advanced topics such as deep learning-based recommendation models, reinforcement learning, and context-aware systems. Significant emphasis is placed on scalable architectures, covering distributed computing frameworks, microservices, cloud-native deployments, and real-time data processing pipelines. In addition, the book explores critical engineering considerations such as data preprocessing, feature engineering, evaluation metrics, model optimization, explainability, fairness, and privacy. Special attention is given to industrial use cases, demonstrating how leading organizations design and deploy recommender systems at scale to handle millions of users and transactions efficiently. These case studies provide valuable insights into performance optimization, system reliability, and business impact. This book is intended for a broad audience, including undergraduate and postgraduate students, researchers, data scientists, software engineers, and industry professionals seeking to understand or implement recommender systems. It can serve both as a textbook for academic courses and as a practical guide for professionals working in the field. The editors express their sincere gratitude to all contributing authors for their dedication and scholarly efforts in bringing this work to fruition. We also extend our appreciation to reviewers, technical editors, and publishers for their valuable support in ensuring the quality and relevance of this volume. We hope this book will serve as a valuable reference and inspire further research and innovation in the field of recommender systems engineering. As technology continues to evolve, the integration of intelligent recommendation mechanisms will remain central to creating personalized, efficient, and impactful digital experiences.

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