Frontier Practice of Deep Learning Recommender Systems

Zhe Wang; Chao Pu; Felice Wang

doi:10.1017/9781009447515.010

8 - Frontier Practice of Deep Learning Recommender Systems

Published online by Cambridge University Press: 08 May 2025

Zhe Wang ,

Chao Pu and

Felice Wang

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Summary

This chapter explores the integration of deep learning in recommender systems, highlighting its significance as a leading application area with substantial business value. We examine notable advancements driven by industry leaders like Meta, Google, Airbnb, and Alibaba. These innovations mark a transformative shift toward deep learning in recommender systems, evidenced by Alibaba’s ongoing innovations in e-commerce and Airbnb’s applications in search and recommendation. For aspiring recommender system engineers, the current era of open-source code and knowledge sharing provides unparalleled access to cutting-edge applications and insights from industry pioneers. This chapter aims to build a foundational understanding of deep learning recommender systems developed by Meta, Airbnb, YouTube, and Alibaba, encouraging readers to focus on technical details and engineering practices for practical application.

Keywords

industrial deep learning recommender system engineering practices practical deep learning applications

Type: Chapter
Information: Deep Learning Recommender Systems , pp. 248 - 282

DOI: https://doi.org/10.1017/9781009447515.010 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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