Description:
This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
Review Quotes:
"One of the standout features of the book is its practical application. Readers are guided through Microsoft's open-source project Microsoft Recommenders, which provides hands-on experience with real-world code examples. This practical focus is immensely valuable for professionals looking to build accurate and efficient recommender systems from scratch. The book is suitable for both students and seasoned professionals, offering a deep understanding of both the theoretical and practical aspects of recommendation algorithms." (Wael Badawy, Computing Reviews, November 13, 2024)