An Improved Algorithm for Recruitment Text Categorization

Abstract

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.