Book contents
- Deep Learning Recommender Systems
- Reviews
- Deep Learning Recommender Systems
- Copyright page
- Contents
- Foreword
- Preface
- 1 Growth Engine of the Internet
- 2 Pre-Deep Learning Era
- 3 Top of the Tide
- 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 Systems
- 9 Build Your Own Recommender Systems Knowledge Framework
- Afterword
- References
2 - Pre-Deep Learning Era
The Evolution of Recommender Systems
Published online by Cambridge University Press: 08 May 2025
Book contents
- Deep Learning Recommender Systems
- Reviews
- Deep Learning Recommender Systems
- Copyright page
- Contents
- Foreword
- Preface
- 1 Growth Engine of the Internet
- 2 Pre-Deep Learning Era
- 3 Top of the Tide
- 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 Systems
- 9 Build Your Own Recommender Systems Knowledge Framework
- Afterword
- References
Summary
Recommender systems have evolved significantly in response to growing demands, progressing from early methods like Collaborative Filtering (CF) and Logistic Regression (LR) to more advanced models such as Factorization Machines (FM) and Gradient Boosting Decision Trees (GBDT). Since 2015, deep learning has become the dominant approach, leading to the development of hybrid and multimodel frameworks. Despite the rise of deep learning models, traditional recommendation methods still hold valuable advantages due to their interpretability, efficiency, and ease of deployment. Furthermore, these foundational models, such as CF, LR, and FM, form the basis for many deep learning approaches. This chapter explores the evolution of traditional recommendation models, detailing their principles, strengths, and influence on modern deep learning architectures, offering readers a comprehensive understanding of this foundational knowledge.
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- Deep Learning Recommender Systems , pp. 12 - 47Publisher: Cambridge University PressPrint publication year: 2025
References
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