“RECOMMENDER SYSTEMS” is a comprehensive guide tailored for students, educators, and professionals interested in understanding the foundation and evolution of personalized recommendation technologies. Developed in alignment with the CCS360 curriculum, this book explores both the theoretical underpinnings and practical implementations of recommender systems in today’s data-driven world.

Covering a range of topics including content-based filtering, collaborative filtering, hybrid methods, deep learning approaches, and real-world applications, the book balances clarity with technical depth, making it suitable for both beginners and advanced learners. With illustrative examples, algorithms, and exercises, this book serves as a reliable resource for mastering one of the most impactful fields in AI and machine learning.

Whether you’re a university student preparing for exams, a teacher seeking structured material, or a data enthusiast eager to explore personalization algorithms, this book is your go-to guide for building and understanding modern recommender systems.

Nancy Chitra Thilaga, M.E. in Applied Electronics, is an Assistant Professor at Grace College of Engineering, Thoothukudi. She brings a unique blend of academic insight and industry experience, having spent four years in the IT sector before transitioning to academia. Her exposure to real-world technological challenges enhances both her teaching and writing.

With a strong academic foundation and a deep interest in emerging technologies, she delivers content that is both conceptually clear and practically relevant. Her focus on curriculum-aligned material and application-oriented perspectives makes this book a valuable contribution to the academic literature in the fields of recommender systems and artificial intelligence.