Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest-Reference-Cited by-同舟云学术

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Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Published:2022-11-30 Issue:6 Volume:37 Page:1444-1463
ISSN:1000-9000
Container-title:Journal of Computer Science and Technology
language:en
Short-container-title:J. Comput. Sci. Technol.

Author:

Shao Ming-Min,Jiang Wen-Jun,Wu Jie,Shi Yu-Qing,Yum TakShing,Zhang Ji

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11390-021-2124-z.pdf

Reference50 articles.

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2. Zewail-Foote M. Pivoting an upper-level, project-based biochemistry laboratory class to online learning during COVID-19: Enhancing research skills and using community outreach to engage undergraduate students. Journal of Chemical Education, 2020, 97(9): 2727-2732. DOI: https://doi.org/10.1021/acs.jchemed.0c00543.

3. Gong J B, Zhao Y, Chen S et al. Hybrid deep neural networks for friend recommendations in edge computing environment. IEEE Access, 2020, 8: 10693-10706. DOI: https://doi.org/10.1109/ACCESS.2019.2958599.

4. He C B, Li H C, Fei X et al. A topic community-based method for friend recommendation in large-scale online social networks. Concurrency and Computation: Practice and Experience, 2017, 29(6): Article No. e3924. DOI: https://doi.org/10.1002/cpe.3924.

5. Cheng Z Y, Ding Y, Zhu L et al. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proc. the 2018 World Wide Web Conference, April 2018, pp.639-648. DOI: 10.1145/3178876.3186145.

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