Pre-Deep Learning Era

Zhe Wang; Chao Pu; Felice Wang

doi:10.1017/9781009447515.004

2 - Pre-Deep Learning Era

The Evolution of Recommender Systems

Published online by Cambridge University Press: 08 May 2025

Zhe Wang ,

Chao Pu and

Felice Wang

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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.

Keywords

traditional recommendation models collaborative filtering (CF)logistic regression (LR)factorization machines gradient boosting decision trees (GBDT)deep factorization machine neural factorization machine

Type: Chapter
Information: Deep Learning Recommender Systems , pp. 12 - 47

DOI: https://doi.org/10.1017/9781009447515.004 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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