A Novel Scheme for Recruitment Text Categorization Based on KNN Algorithm

  • Wenshuai Qin
  • Wenjie Guo
  • Xin LiuEmail author
  • Hui Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)


With the rapid development of the Internet, online recruitment has gradually become mainstream. However, job seekers need to spend a lot of time to find a suitable job when there are a large variety of job information, which will seriously affect their efficiency, so it is necessary to carry out more detailed and efficient classification of the recruitment documents. Currently, common text classification algorithms include KNN (k-Nearest Neighbor), SVM (Support Vector Machine) and Naïve Bayes. Particularly, KNN algorithm is widely used in text classification for its simple implementation and accurate classification. But KNN algorithm has been criticized for its inefficiency in the face of large-scale recruitment. This paper improves the original KNN algorithm and proposes RS-KNN algorithm to achieve rapid refinement and classification of recruitment information. Experiments show that the improved algorithm has higher efficiency and less time consumption than the original algorithm.


KNN Text classification Feature extraction Job classification 



This work is supported by Postgraduate Education Innovation and Quality Improvement Project of Henan University, Henan University (SYL18020105).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of SoftwareHenan UniversityKaifengChina
  2. 2.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina

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