Job Title Prediction as a Dual Task of Expertise Prediction in Open Source Software

Abstract

Career path prediction is an important task in computational jobs marketplace. Recent advances in data science and artificial intelligence have imposed a huge recruitment demand on talents in the IT field. Previous studies predict a talent’s next job title solely based on her past experience in the resume, which can lead to errors if the resume contains fake information. With the popularity of open-source software, we argue that the next job title can be predicted based on a candidate’s past expertise in the open-source community. On the other hand, the career path can also affect the development of a talent’s expertise. Motivated by the observation, we propose to predict the job titles of IT talents as a dual task of forecasting their expertise development in open-source software. To solve the task, we design a dual learning model DualJE that leverages both the data-level and model-level duality. Experimental results show that DualJE is effective and performs much better than comparative models. A replication package for this work is available at https://github.com/DaSESmartEdu/DualJE .