Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit-Reference-Cited by-同舟云学术

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Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit

Published:2021 Issue: Volume: Page:359-375
ISSN:0302-9743
Container-title:Database Systems for Advanced Applications
language:
Short-container-title:

Author:

Fu Bin,Liu Hongzhi,Zhu Yao,Song Yang,Zhang Tao,Wu Zhonghai

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-73197-7_24

Reference17 articles.

1. Li, S., et al.: Deep job understanding at linkedin. In: SIGIR, pp. 2145–2148. ACM (2020)

2. Zhu, C., et al.: Person-job fit: adapting the right talent for the right job with joint representation learning. ACM Trans. Manage. Inf. Syst. 9(3), 12:1–12:17 (2018)

3. Qin, C., et al.: Enhancing person-job fit for talent recruitment: an ability-aware neural network approach. In: SIGIR, pp. 25–34. ACM (2018)

4. Luo, Y., Zhang, H., Wen, Y., Zhang, X.: Resumegan: an optimized deep representation learning framework for talent-job fit via adversarial learning. In: CIKM, pp. 1101–1110. ACM (2019)

5. Le, R., Hu, W., Song, Y., Zhang, T., Zhao, D., Yan, R.: Towards effective and interpretable person-job fitting. In: CIKM, pp. 1883–1892. ACM (2019)

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