research-article

AutoML for Deep Recommender Systems: A Survey

Authors:

Hongzhi YinAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 101, Pages 1 - 38

https://doi.org/10.1145/3579355

Published: 22 March 2023 Publication History

Abstract

Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep learning, deep recommender systems show superior performance by capturing non-linear information and item-user relationships. However, the design of deep recommender systems heavily relies on human experiences and expert knowledge. To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems. This survey performs a comprehensive review of the literature in this field. First, we propose an abstract concept for AutoML for deep recommender systems (AutoRecSys) that describes its building blocks and distinguishes it from conventional AutoML techniques and recommender systems. Second, we present a taxonomy as a classification framework containing feature selection search, embedding dimension search, feature interaction search, model architecture search, and other components search. Furthermore, we put a particular emphasis on the search space and search strategy, as they are the common thread to connect all methods within each category and enable practitioners to analyze and compare various approaches. Finally, we propose four future promising research directions that will lead this line of research.

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 22 March 2023

Online AM: 05 January 2023

Accepted: 29 December 2022

Revised: 19 November 2022

Received: 29 July 2022

Published in TOIS Volume 41, Issue 4

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National Natural Science Foundation of China
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Program for Guangdong Introducing Innovative and Entrepreneurial Teams

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