Deep Learning Recommender Systems

Name: Deep Learning Recommender Systems
Brand: Atlantic Books
SKU: 9781009447508
Price: 6353.0 INR
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by Zhe Wang , Chao Pu , Felice Wang

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ISBN13: 9781009447508
Binding: Paperback
Subject: N/A
Publisher: Cambridge University Press
Publisher Imprint: Cambridge University Press
Publication Date: May 22, 2025
Pages: 313
Original Price: USD 64.99
Language: English
Edition: N/A
Item Weight: 504 grams
BISAC Subject(s): Database Administration & Management

About The Book

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.

About The Author

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.

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.

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.

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