Deep Learning Recommender Systems

‘Recommender systems hold immense commercial value, and deep learning is taking them to the next level. This book focuses on real-world applications, equipping engineers with the tools to build smarter, more effective recommendation systems. With a clear and practical approach, this book is an essential guide to mastering the latest advancements in the field.’ Yue Zhuge, NGP Capital‘Reading this book allows you to witness the wealth of resources and engineering practices driving recommendation system development. The authors share unique insights into bridging academic research and industry applications, providing valuable technical perspectives for practitioners and students. The book emphasizes innovative thinking and inspires readers to develop new solutions in recommendation system technologies.’ Zi Yang, Google DeepMind

Zhe Wang is an engineering director at Disney Streaming, leading a machine learning team. He has more than ten years of experience working in the field of recommender systems and computational advertising. He has published more than ten academic papers and three technical books, with more than 100,000 readers.

Chao Pu is a machine learning engineer with extensive experience in scalable machine learning system at large scale IT companies. He has designed, developed, operated and optimized multiple recommendation systems that serve millions of customers.

Felice Wang is a data scientist with a wealth of experience of creating analytics models, such as predicting customer retention and optimizing price. She has also implemented machine learning techniques to build data-driven resolutions for various business circumstances.