AutoML for Deep Recommender Systems: A Survey

Zheng, Ruiqi1; Qu, Liang1; Cui, Bin2; Shi, Yuhui3; Yin, Hongzhi1

2023-10-01

10.1145/3579355

ACM TRANSACTIONS ON INFORMATION SYSTEMS   Impact Factor & Quartile

1046-8188

1558-2868

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.

AutoML survey taxonomy

SCI

English

Corresponding

Australian Research Council["FT210100624","DP190101985"] ; National Natural Science Foundation of China[61761136008] ; Shenzhen Fundamental Research Program[JCYJ20200109141235597] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110024] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; Program for Guangdong Introducing Innovative and Entrepreneurial Teams[2017ZT07X386]

Computer Science

Computer Science, Information Systems

WOS:001068685300020

ASSOC COMPUTING MACHINERY

COMPUTER SCIENCE

Web of Science

1.Univ Queensland, Brisbane, Qld 4072, Australia
2.Peking Univ, 5 Yiheyuan Rd, Beijing 100871, Peoples R China
3.Southern Univ Sci & Technol, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, Peoples R China

Zheng, Ruiqi,Qu, Liang,Cui, Bin,et al. AutoML for Deep Recommender Systems: A Survey[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2023,41(4).

Zheng, Ruiqi,Qu, Liang,Cui, Bin,Shi, Yuhui,&Yin, Hongzhi.(2023).AutoML for Deep Recommender Systems: A Survey.ACM TRANSACTIONS ON INFORMATION SYSTEMS,41(4).

Zheng, Ruiqi,et al."AutoML for Deep Recommender Systems: A Survey".ACM TRANSACTIONS ON INFORMATION SYSTEMS 41.4(2023).