Job Prediction: From Deep Neural Network Models to Applications

TL;DR

This paper explores deep neural network models for job prediction based on descriptions, proposing an ensemble approach that outperforms individual models with an F1 score of 72.71%.

Contribution

It introduces a novel ensemble model combining multiple deep neural networks for improved job prediction accuracy.

Findings

Ensemble model achieved highest F1 score of 72.71%.

Deep neural networks outperform traditional methods.

Insights provided for future improvements in job prediction.

Abstract

Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT Job dataset. In addition, we also proposed a simple and effective ensemble model combining different deep neural network models. The experimental results illustrated that our proposed ensemble model achieved the highest result with an F1 score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.