The Application of Deep Learning Model in Recruitment Decision

Abstract

With the rapid development of social economy, the competition of human resources is becoming more and more fierce. Recruitment, as the main way for enterprises to obtain talents, determines the future development of enterprises to a great extent. Compared with Western advanced countries, the research on recruitment in China started late, the overall research level is relatively backward, and most of the relevant technical means and analytical methods are introduced from advanced countries. The existing literature is still relatively scattered, which is not conducive to the rapid development of recruitment direction research, and is not conducive to specific applications. Starting with the existing deep learning, from four models, that is, based on the traditional machine learning model, conditional random field (CRF), deep learning models Bi-LSTM-CRF, BERT, and BERT-Bi-LSTM-CRF identify and automatically extract recruitment entities and study recruitment accordingly; BERT-Bi-LSTM-CRF-BERT and BERT-BiLSTM-CRF are the models with the worst recognition effect. Although they have stronger text feature extraction ability and context information capture ability, they are limited by the small scale of information science recruitment corpus and the small number of entities, so their performance under this task is not brought into full play. Although CRF is relatively traditional, it can still achieve excellent results on some small-scale sparse datasets.