Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation-Reference-Cited by-同舟云学术

Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation

Author:

Hao Yongjing¹^ORCID,Zhang Tingting²,Zhao Pengpeng¹^ORCID,Liu Yanchi³,Sheng Victor S.⁴^ORCID,Xu Jiajie¹^ORCID,Liu Guanfeng⁵^ORCID,Zhou Xiaofang⁶^ORCID

Affiliation:

1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

2. Huawei Technologies Co., Ltd, Nanjing, China

3. Rutgers University, New Brunswick, NJ, USA

4. Department of Computer Science, Texas Tech University, Lubbock, TX, USA

5. Macquarie University, Sydney, Australia

6. Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

Funder

National Natural Science Foundation of China

Universities of Jiangsu Province

Suzhou Science and Technology Development Program

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Priority Academic Program Development of Jiangsu Higher Education Institutions

Exploratory Self-selected Project of the State Key Laboratory of Software Development Environment

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

Reference60 articles.

1. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics;gutmann;J Mach Learn Res,2012

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