Employment skill recommendation of college students based on parallel FP-growth algorithm

Yingzhuo Xu; Yifan Wang

doi:10.1117/12.2622715

1 December 2021 Employment skill recommendation of college students based on parallel FP-growth algorithm

Yingzhuo Xu, Yifan Wang

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 1207907 (2021) https://doi.org/10.1117/12.2622715
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

The degree of matching between the skills acquired by college students when applying for jobs and the skills required by companies for online recruitment of talents is low, resulting in a low employment rate. In response to this problem, it is proposed to apply the Spark-based parallel FP-Growth algorithm to the analysis of job requirements for recruitment information, extract the job skills corresponding to the job, and improve the matching degree between graduates and the recruitment market demand. Compared with the traditional FP-Growth algorithm, the Spark-based parallel FP-Growth algorithm has better mining for massive data sets.

Citation Download Citation

Yingzhuo Xu and Yifan Wang "Employment skill recommendation of college students based on parallel FP-growth algorithm", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 1207907 (1 December 2021); https://doi.org/10.1117/12.2622715

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