Abstract
Human resource network information recommendation method is affected by similarity information, which leads to poor recommendation effect. A human resource network information recommendation method based on machine learning is proposed. The machine learning statistical model is established to filter the noise information. Temporal behavioral preference features were constructed to calculate the average number of information clicks. Data preprocessing is accomplished through data format conversion, data cleaning and data specification. The network information resources were retrieved, the pattern matching results were obtained, the loss function based on content association rules was constructed, and the recommendation model was constructed combined with hybrid genetic algorithm. The experimental results show that the highest recommendation accuracy of this method reaches 94%, and the highest recall rate is 0.90, which indicates that the application of research methods to recommend human resources network information has a good effect.
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References
Yanbo, L., Xueyan, W., Kesheng, X.: Architecture and optimization of recommendation system based on Boosting framework. Modern Electronics Technique 43(08), 19–21+28 (2020)
Yazhi, Y., Yong, Z., Jun, L.: Intelligent information recommendation model construction simulation based on knowledge graph. Computer Simulation 38(01), 437–440+480 (2021)
Jihui, F., Shaohua, T.: Research on the application of improved machine learning collaborative recommendation algorithm in intelligent control. Machine Tool & Hydraulics 48(18), 177–182 (2020)
Min, Z., Yixuan, Z.: Application of machine learning in human resource management. Human Resources Dev. China 39(01), 71–83 (2022)
Yan, L.: Library bibliographic collaborative recommendation system based on machine learning algorithm. Modern Electronics Technique 43(14), 180–182,186 (2020)
Yue, G., Dan, L., Kaihui, G.: Research on network traffic classification based on machine learning and deep learning. Telecommunications Science 37(03), 105–113 (2021)
Daoheng, Z., Zhiqiang, L.: Parallel calculation method for maximum information coefficient. Science Technol. Eng. 21(34), 14625–14633 (2021)
Jiaxing, W., Dapeng, T., Bin, C., et al.: Independent path-based process recommendation algorithm for improving biomedical process modelling. Electron. Lett. 56(7), 531–533 (2020)
Haozheng, Z., Ming, H., Jingjing, Y., et al.: A data preprocessing method for automatic modulation classification based on CNN. IEEE Communications Letters: A Publication of the IEEE Communications Society 25(4), 1206–1210 (2021)
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School level undergraduate teaching reform project of China University of Labor Relations:Research on the teaching reform of collective negotiation system and Practice (Project No.: 2021JG07).
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Wang, X. (2023). Human Resource Network Information Recommendation Method Based on Machine Learning. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_2
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DOI: https://doi.org/10.1007/978-3-031-20102-8_2
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