Deep Learning Recommender Systems
Deep Learning Recommender Systems
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.
- Explores the application of deep learning technology in recommendation systems, with plentiful real-life examples
- Introduces the development history of recommendation systems and the evolution diagram of recommendation models
- Teaches readers to effectively solve engineering problems, as well as developing understanding of the theoretical concepts
Product details
No date availablePaperback
9781009447508
400 pages
244 × 170 mm
Table of Contents
- 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.
