Deep Learning Recommender Systems

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.

'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

• 1. Growth engine of the internet – recommender system; 2. Pre-deep learning era–the evolution of recommender systems; 3. Top of the tide – application of deep learning in recommendation system; 4. Application of embedding technology in recommender systems; 5. Recommender systems from multiple perspectives; 6. Engineering implementations in deep learning recommender systems; 7. Evaluation in recommender systems; 8. Frontier practice of deep learning recommender system; 9. Build your own recommender system knowledge framework; Afterword.