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Designing a Human Enterprise Management Model Using Deep Learning and Wireless Connectivity

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Abstract

As the Internet era has progressed, more and more candidates are getting their recruitment information online. At the same time, a problem of information overload for human enterprise management services has arisen due to the massive volume of information. The main instrument to combat information overload in the Internet era has emerged as the human enterprise management system (HEMS). It can actively search through the information overload to discover and present the material that people are interested in. To ensure wide spread adaptability, the role of wireless communication for HEMS cannot be ignored. Wireless connectivity ensures that plethora of HEMS-related information can be accessed and managed effectively around the globe. To extract features from this massive information, deep learning can be used. There has not been much progress in the area of HEM systems as far as deep learning is concerned. This paper proposes an HDCF algorithm, which resolves the main issues of data sparseness and cold start in conventional collaborative filtering algorithms with the aid of deep learning feature extraction capabilities and wireless connectivity. The HDCF is essentially a recommendation algorithm that suggests to newly registered users the most prevalent and recent jobs in the system and employs an online algorithm based on content filtering to calculate the candidate’s rating for the most recently listed positions. According to the testing findings, the HDCF algorithm performed better than existing human business management algorithms like probability matrix factorization (PMF) and content-based filtering (CBF).

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Liu Y (2023) “Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources,” Journal of Applied Data Sciences, vol. 4, no. 2, Art. no. 2, Mar. doi: https://doi.org/10.47738/jads.v4i2.85

  2. Nassar N, Jafar A, Rahhal Y (May 2020) Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization. J Big Data 7(1):34. https://doi.org/10.1186/s40537-020-00309-6

  3. Alzubaidi L et al (2021) “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, p. 53, doi: https://doi.org/10.1186/s40537-021-00444-8

  4. Zheng K, Yang X, Wang Y, Wu Y, Zheng X (Jan. 2020) Collaborative filtering recommendation algorithm based on variational inference. Int J Crowd Sci 4(1):31–44. https://doi.org/10.1108/IJCS-10-2019-0030

  5. Malinowski J, Keim T, Wendt O, Weitzel T (2006) “Matching People and Jobs: A Bilateral Recommendation Approach,” in Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), Jan. pp. 137c–137c. doi: https://doi.org/10.1109/HICSS.2006.266

  6. Ming F, Tan L, Cheng X (2021) “Hybrid Recommendation Scheme Based on Deep Learning,” Mathematical Problems in Engineering, vol. p. e6120068, Dec. 2021, doi: https://doi.org/10.1155/2021/6120068

  7. Liu, H., Liu, M., Li, D., Zheng, W., Yin, L.,... Wang, R. (2022). Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics, 11(20). doi: https://doi.org/10.3390/electronics11203264.

  8. Chen H, Xiong Y, Li S, Song Z, Hu Z, Liu F (2022) Machines 10(2):155. https://doi.org/10.3390/machines10020155. Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode

  9. Zhu H, Research on Human Resource Recommendation Algorithm Based on Machine Learning (2021) “,” Scientific Programming, vol. p. e8387277, Aug. 2021, doi: https://doi.org/10.1155/2021/8387277

  10. Zhou D (2022) “Application of Data Mining Technology in Enterprise Digital Human Resource Management,” Security and Communication Networks, vol. p. e7611623, Jun. 2022, doi: https://doi.org/10.1155/2022/7611623

  11. Longo F, Nicoletti L, Padovano A (2017) “Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context,” Computers & Industrial Engineering, vol. 113, pp. 144–159, Nov. doi: https://doi.org/10.1016/j.cie.2017.09.016

  12. Roy PK, Chowdhary SS, Bhatia R (Jan. 2020) A machine learning approach for automation of resume recommendation system. Procedia Comput Sci 167:2318–2327. https://doi.org/10.1016/j.procs.2020.03.284

  13. Salakhutdinov R, Mnih A, Hinton G (2007) “Restricted Boltzmann machines for collaborative filtering,” presented at the ACM International Conference Proceeding Series, Jun. pp. 791–798. doi: https://doi.org/10.1145/1273496.1273596

  14. Wang H, Wang N, Yeung D-Y (2015) “Collaborative Deep Learning for Recommender Systems,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in KDD ’15. New York, NY, USA: Association for Computing Machinery, Aug. pp. 1235–1244. doi: https://doi.org/10.1145/2783258.2783273

  15. Wei J, He J, Chen K, Zhou Y, Tang Z (Mar. 2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:29–39. https://doi.org/10.1016/j.eswa.2016.09.040

  16. Al-Otaibi S, Ykhlef M (2017) “Hybrid immunizing solution for job recommender system,” Frontiers of Computer Science, vol. 11, no. 3, Art. no. 3, May doi: https://doi.org/10.1007/s11704-016-5241-z

  17. Wu L, Shah S, Choi S, Tiwari M, Posse C (2014) “The Browsemaps: collaborative filtering at LinkedIn,” presented at the RSWeb@RecSys, Accessed: May 11, 2023.

  18. Shani G, Heckerman D, Brafman RI “An MDP-Based Recommender System”

  19. Dou Y, Yang H, Deng X (2016) A Survey of Collaborative Filtering Algorithms for Social Recommender Systems. Aug 40–46. https://doi.org/10.1109/SKG.2016.014

  20. Hu D, Chen L (2016) “A High Efficient Recommendation Algorithm Based on LDA,” in Human Centered Computing, Q. Zu and B. Hu, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 668–675. doi: https://doi.org/10.1007/978-3-319-31854-7_61

  21. Ling-jun ZOU and C. L. and, Ling-jun LJZOU (2018) “A Time-Weighted Hybrid Recommendation Algorithm,” Computer Science, vol. 43, no. Z11, pp. 451–454, Dec. doi: https://doi.org/10.11896/j.issn.1002-137X.2016.11A.101

  22. Adomavicius G, Tuzhilin A (2005) “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,”IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, Jun. doi: https://doi.org/10.1109/TKDE.2005.99

  23. Qin X, Ban Y, Wu P, Yang B, Liu S, Yin L, Liu M, Zheng W (2022) Improved image Fusion Method based on sparse decomposition. Electronics 11(15):2321. https://doi.org/10.3390/electronics11152321

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  1. Shandong Labor Vocational and Technical College, Shandong, 250000, China

    Zhenxing Song, Di Zhang & Yue Wang

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  1. Zhenxing Song
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  2. Di Zhang
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  3. Yue Wang
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Song, Z., Zhang, D. & Wang, Y. Designing a Human Enterprise Management Model Using Deep Learning and Wireless Connectivity. Mobile Netw Appl 28, 2162–2170 (2023). https://doi.org/10.1007/s11036-023-02173-z

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