Self-Supervised Learning for Recommender Systems: A Survey-Reference-Cited by-同舟云学术

Self-Supervised Learning for Recommender Systems: A Survey

Published:2024-01 Issue:1 Volume:36 Page:335-355
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Yu Junliang¹^ORCID,Yin Hongzhi¹^ORCID,Xia Xin¹^ORCID,Chen Tong¹^ORCID,Li Jundong²^ORCID,Huang Zi¹^ORCID

Affiliation:

1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

2. Department of Electrical and Computer Engineering, Department of Computer Science, School of Data Science, University of Virginia, Charlottesville, VA, USA

Funder

ARC Future Fellowship

ARC Industrial Transformation Training Centre for Uniquely Australian Foods

Discovery Project

Discovery Early Career Researcher Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10348031/10144391.pdf?arnumber=10144391

Reference148 articles.

1. Introduction to Recommender Systems Handbook

2. Deep Neural Networks for YouTube Recommendations

3. Wide & Deep Learning for Recommender Systems

4. Deep Interest Network for Click-Through Rate Prediction

5. Try This Instead

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