The Application of Deep Learning Model in Recruitment Decision

Yang Wang,Zhengjie Zhu,Xin Ning

doi:10.1155/2022/9645830

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.

Highlights

In recent years, deep artificial neural networks have won numerous competitions in the fields of pattern recognition and machine learning [1]
The results show that the improvement of recruitment entity identification experiment in this paper is effective; on the other hand, it shows that conditional random field (CRF) basically meets the requirements of this study
In BERT-Bi-Long-short-term memory (LSTM)-CRF model, the difference between group 2 with the highest F value of 81.03% and group 9 with the lowest F value of 75.41% is 4.84%, which once again shows that corpus quality has great influence

Summary

Introduction

Deep artificial neural networks have won numerous competitions in the fields of pattern recognition and machine learning [1]. The key challenge of face recognition can be solved by deep learning and using face recognition [3] and verification signals as supervision to reduce personal internal differences and expand personal differences at the same time; among them, convolution network has become the preferred method for analyzing medical images [4, 5]; by taking MNIST as an example to show the uncertainty of the model, the uncertainty of the model in Bayesian pipeline and deep reinforcement learning are regarded as a practical task [6]; “deep learning” technology determines sequence specificity from experimental data, which provides an scalable, flexible, and unified calculation method for pattern discovery [7, 8]; the inception module in convolution neural network [9] is interpreted as an intermediate step between conventional convolution and deep separable convolution operation From this point of view, deep separable convolution can be understood as Inception module with the largest number of towers. Through the previous definition of educational background, ability, specialty, character, and experience, B is the beginning word

Methods

Results

Conclusion