Top of the Tide

Zhe Wang; Chao Pu; Felice Wang

doi:10.1017/9781009447515.005

3 - Top of the Tide

Application of Deep Learning in Recommender Systems

Published online by Cambridge University Press: 08 May 2025

Zhe Wang ,

Chao Pu and

Felice Wang

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Summary

The introduction of advanced deep learning models such as Microsoft’s Deep Crossing, Google’s Wide&Deep, and others like FNN and PNN in 2016 marked a significant shift in the field of recommender systems and computational advertising, establishing deep learning as the dominant approach. This chapter discusses the evolution of traditional recommendation models and highlights two main advancements in deep learning models: enhanced expressivity for uncovering hidden data patterns and flexible model structures tailored to specific business use cases. Drawing on techniques from computer vision, speech, and natural language processing, deep learning recommendation models have rapidly evolved. The chapter summarizes several influential deep learning models and constructs an evolution map. These models are selected based on their industry impact and their role in advancing deep learning recommender systems. Additionally, the chapter will introduce applications of Large Language Models (LLMs) in recommender systems, exploring how these models further enhance recommendation technologies.

Keywords

deep learning recommendation models deep crossing models wide&deep models factorization neural network product neural network attention mechanism transformer large language models (LLM)

Information

Type: Chapter
Information: Deep Learning Recommender Systems , pp. 48 - 116

DOI: https://doi.org/10.1017/9781009447515.005 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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