Recommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.
Recommender systems are ubiquitous in our lives and are one of the main monetization methods of the Internet. This book explores how to apply deep learning technology in recommender systems, helping practitioners and researchers to understand the engineering implementation solutions of industry giants and cutting-edge progress in the field.
Arvustused
'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
Muu info
Discover cutting-edge applications of deep learning in recommender systems, one of the main monetization methods of the Internet.
1. Growth engine of the internet recommender system;
2. Pre-deep
learning erathe 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.
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.
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