Deep Learning Recommender Systems

Erscheinungsjahr 2025
ISBN: 978-1-009-44750-8
Verlag: Cambridge University Press

Buch, Englisch, 400 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 545 g

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.

Autoren/Hrsg.

Wang, Zhe

Pu, Chao

Wang, Felice

Inhaltsverzeichnis

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.

Über Autor(innen)

Wang, Zhe
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.

Wang, Felice
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.

Pu, Chao
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.