Research on Two-Way Human Resource Recommendation Method Considering User Privacy

DOI

10.2991/978-94-6463-034-3_14How to use a DOI?

Keywords

user privacy; human resources; two-way recommendation; enterprise recruitment; employment information; data anonymity; big data

Abstract

Traditional two-way human resource recommendation method has the problem of incomplete data anonymous process, which leads to low recommendation accuracy. Therefore, a two-way human resource recommendation method considering user privacy is designed. Collect recruitment information from human resources big data, and convert file formats in batches. We vectorizing each piece of information, build user privacy protection model, calculate entropy redundancy of data attribute value, optimize data anonymous process, calculate similarity between users according to preference score, and set two-way recommendation mode of human resources. Experimental results: The average recommendation accuracy of the two-way human resource recommendation method in this paper and the other two two-way human resource recommendation methods are 55.645%, 48.363% and 48.267% respectively, indicating that the two-way human resource recommendation method has a wider application scope on the basis of combining user privacy.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiangjing Lin
AU  - Chunliang Fu
AU  - Linhua Gong
AU  - Ming Guo
PY  - 2022
DA  - 2022/12/23
TI  - Research on Two-Way Human Resource Recommendation Method Considering User Privacy
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 120
EP  - 131
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_14
DO  - 10.2991/978-94-6463-034-3_14
ID  - Lin2022
ER  -