Job Prediction: From Deep Neural Network Models to Applications
This work addresses job matching for students and job seekers, but it is incremental as it applies existing methods to a specific domain.
The paper tackled job prediction by evaluating deep neural network models and proposing an ensemble approach, achieving a 72.71% F1 score on an IT Job dataset.
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.