Название: Deep Learning Recommender Systems
Автор: Zhe Wang, Chao Pu, Felice Wang
Издательство: Cambridge University Press
Год: 2025
Страниц: 314
Язык: английский
Формат: pdf
Размер: 24.6 MB

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.

This book hopes to discuss the “classic” or “cutting-edge” technical content related to recommender systems, with a particular focus on the application of Deep Learning in the industry. It should be noted that this book is not an introductory book on Machine Learning or Deep Learning. Although the book will intersperse the ­introduction of basic Machine Learning knowledge, most of the content assumes that readers have some Machine Learning background. Additionally, this book is not a purely theoretical book, but a technical book that introduces the a­pplication ­methods of Deep Learning in the field of recommender systems and industry ­cutting-edge technology related to recommender systems from the perspective of engineers’ practical experience.

Readers of This Book:
The target audience for this book can be divided into two categories.

The first group consists of professionals in the internet industry, especially those in the fields of recommendation, advertising, and search. By studying this book, these peers can become familiar with the development of Deep Learning recommender systems and the details of each key model and technology. This could enable them to apply or even improve these technologies in their work.

The second group consists of enthusiasts and students with some background in Machine Learning who wish to enter the field of recommender systems. This book aims to introduce the relevant principles and application methods of recommender system technology from a practical perspective, starting with the details and using plain language, helping readers to build a cutting-edge and practical knowledge framework of recommender systems from scratch.