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.
Cite this article
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 -