Abstract
Currently when university students are going into job market, it is found a lot of challenges to help the students and also employers to help their efficiency in finding matching degree. However, during the job seeking peak time, for example, an event called “job fair” in China, it is found very challenging for employer to quickly filter potentially qualified applicants since an employer will probably receive huge number of resumes in a very short period. To solve this problem, in this research we proposed a student file based employer oriented job recommendation framework. In this system, a student is firstly modelled by the personal features and also academic features. Afterwards, different similarity mechanism between the fresh students with those recruited in the target employers are designed to help recommend students. Furthermore, a dynamic recruitment size aware strategy is also proposed to further polish the recommendation results. The experimental study on a Chinese university’s real recruitment data has shown its potential in real applications.
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Acknowledgments
This work was supported by the National High Technology Research and Development Program of China (No. 2013AA01A601), the National Natural Science Foundation of China (No. 61472021), and the Fundamental Research Funds for the Central Universities.
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Liu, R., Ouyang, Y., Rong, W., Song, X., Xie, W., Xiong, Z. (2016). Employer Oriented Recruitment Recommender Service for University Students. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_75
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DOI: https://doi.org/10.1007/978-3-319-42297-8_75
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