Career Trajectory Prediction based on CNN

Abstract

With the rapid development of online recruitment, it is very important for recruitment enterprises to analyze job seekers' experience and recommend suitable and satisfied job to them. This paper describes the dataset of job seekers' resumes from the internet. According to the basic personal information of job seekers such as gender, age, specialty and education, as well as multiple work experiences including working duration, company industry, company scale, monthly salary and position name, we predict job seekers' future job information. Considering that there are both textual data and numerical data in each resume. In order to extract semantic features accurately, the text data is transformed into vectors with the help of Word2Vec provided by Google. Ultimately, we construct a convolutional neural network (CNN) model to address the career trajectory prediction problem. The validity of the model is verified on a real-world dataset with 70,000 resumes.

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