Collaboration-Aware Hybrid Learning for Knowledge Development Prediction-Reference-Cited by-同舟云学术

Collaboration-Aware Hybrid Learning for Knowledge Development Prediction

Published:2024-05-13 Issue: Volume:38 Page:3976-3985
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Chen Liyi¹^ORCID,Qin Chuan²^ORCID,Sun Ying³^ORCID,Song Xin⁴^ORCID,Xu Tong¹^ORCID,Zhu Hengshu⁵^ORCID,Xiong Hui⁶^ORCID

Affiliation:

1. School of Data Science, University of Science and Technology of China, Hefei, China

2. Career Science Lab, BOSS Zhipin & PBC School of Finance, Tsinghua University, Beijing, China

3. Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

4. Baidu Talent Intelligence Center, Baidu Inc., Beijing, China

5. Career Science Lab, BOSS Zhipin, Beijing, China

6. Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) & Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Guangzhou, China

Funder

Guangzhou-HKUST (GZ) Joint Funding Program

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645326

Reference50 articles.

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3. Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, and Enhong Chen. 2020. MMEA: entity alignment for multi-modal knowledge graph. In Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020, Hangzhou, China, August 28--30, 2020, Proceedings, Part I 13. Springer, 134--147.

4. Multi-modal Siamese Network for Entity Alignment

5. Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, Vol. 4, 4 (1994), 253--278.

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