TY - GEN
T1 - An Improved Algorithm for Recruitment Text Categorization
AU - Zhao, Hui
AU - Liu, Xin
AU - Guo, Wenjie
AU - Gai, Keke
AU - Wang, Ying
N1 - Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.
PY - 2019
Y1 - 2019
N2 - With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.
AB - With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.
KW - Feature extraction
KW - Naive Bayes algorithm
KW - Recruitment categorization
KW - Text categorization
UR - http://www.scopus.com/inward/record.url?scp=85076966045&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-1922-2_24
DO - 10.1007/978-981-15-1922-2_24
M3 - Conference contribution
AN - SCOPUS:85076966045
SN - 9789811519215
T3 - Communications in Computer and Information Science
SP - 335
EP - 348
BT - Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings
A2 - Ning, Huansheng
PB - Springer
T2 - 3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019
Y2 - 16 December 2019 through 18 December 2019
ER -