Abstract
Against the increasing severity of employment difficulties, job recommendation systems for college students are becoming increasingly important. As it is impossible to refer to the students’ own employment data, relevant research based on the school’s historical employment data provides a reference for students’ employment. We proposes a method based on the school’s historical employment data for job recommendation for college students. The specific ideas and characteristics are as follows: First, preprocess the collected data to generate the user portrait. In the user portrait construction process, we proposes using the AHP-Entropy Weight method to construct a weight vector of ability requirements for different positions, highlighting the focus of students’ abilities in different positions. To improve computational efficiency, first, we uses clustering algorithms to construct different user groups with different characteristics. Then, we calculates the similarity between the students to be recommended and the user groups, followed by the similarity with the users in that group to improve computational efficiency. In particular, we prove that if the number of samples in the database is greater than or equal to 6, our algorithm will have a lower average time complexity than the traditional algorithm. To address the scarcity of employment market data for college students, we collects the real employment data of graduating classes of a major in a certain university to build the HNNU-JOB data set based on students’ employment ability characteristics. Extensive experiments on HNNU-JOB show that the proposed method achieves remarkable performance of recommendation.
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References
Zhang, Y., Yang, C., Niu, Z.: A research of job recommendation system based on collaborative fltering. In: 2014 Seventh International Symposium on Computational Intelligence and Design, vol. 1, pp. 533–538. IEEE (2014)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)
Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)
Nie, M., Yang, L., Sun, J., et al.: Advanced forecasting of career choices for college students based on campus big data. Front. Comput. Sci. 12, 494–503 (2018)
Nie, M., Xiong, Z., Zhong, R., et al.: Career choice prediction based on campus big data-mining the potential behavior of college students. Appl. Sci. 10(8), 2841 (2020)
Zhu, C., Zhu, H., Xiong, H., et al.: Person-job fit: adapting the right talent for the right job with joint representation learning. ACM Trans. Manag. Inf. Syst. (TMIS) 9(3), 1–17 (2018)
Qin, H., Zhu, T., Xu, C., et al.: An enhanced neural network approach to person-job fit in talent recruitment. ACM Trans. Inf. Syst. 38(2), 1–33 (2020)
Parks, J.B.: Employment status of alumni of an undergraduate sport management program. J. Sport Manag. 5(2), 100–110 (1991)
Xie, C., Dong, D., Shengping, H., et al.: Safety evaluation of smart grid based on AHP-entropy method. Syst. Eng. Procedia 4, 203–209 (2012)
Cooper A., Robert Reimann R., Cronin D.: About Face 3P: The Essentials of Interaction Design, pp. 19–22. Wiley Publishing Inc., New Jersey (2007)
Amato, G., Straccia, U.: User profile modeling and applications to digital libraries. In: Proceedings of the 3rd International Conference on Theory and Practice of Digital Libraries, Paris, France, pp. 184–197 (1999)
Quintana R.M., Haley S.R., Levick A., et al.: The persona party: using personas to design for learning at scale, In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA 2017), pp 933–941 (2017)
Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3), 234–81 (1977)
Saaty, R.W.: The analytic hierarchy process-what it is and how it is used. Math. Model. 9(3–5), 161–176 (1987)
Clausius, R.: On the moving force of heat, and the laws regarding the nature heat itself which are deducible therefrom. Philosoph. Mag. 2(ser. 4), 1–21, 102–119 (1851)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Brendan, J.: Frey Delbert Dueck, clustering by passing messages between data points. Science 315, 972–976 (2007)
Guo, H., Tang, R., Ye, Y., et al.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2782–2788 (2017)
Song, W., Shi, C., Xiao, Z., et al.: AutoInt: automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 1161–1170 (2019)
Xiao, J., Ye, H., He, X., et al.: Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv: 1708.04617 (2017)
Acknowledgements
This work was supported by National Students’ Platform for Innovation and Entrepreneurship Training Program (202210542046) and Hunan Province General Higher Education Teaching Reform Research Project (HNJG-2021-0394).
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He, Y., Cai, M. (2024). Efficient Recommendation Algorithm for Employment of College Students for Various Majors. In: Cai, Z., Xiao, M., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2023. Communications in Computer and Information Science, vol 1944. Springer, Singapore. https://doi.org/10.1007/978-981-99-7743-7_11
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DOI: https://doi.org/10.1007/978-981-99-7743-7_11
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