@inproceedings{423a8657e6d94221befda3c29b17d17c,
title = "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{\textquoteright}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{\textquoteright}s past expertise in the open-source community. On the other hand, the career path can also affect the development of a talent{\textquoteright}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.",
keywords = "API expertise prediction, Dual learning, Job title prediction, Model-level duality, Talent management",
author = "Xin Liu and Yu Wang and Qiwen Dong and Xuesong Lu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024 ; Conference date: 09-09-2024 Through 13-09-2024",
year = "2024",
doi = "10.1007/978-3-031-70381-2\_24",
language = "英语",
isbn = "9783031703805",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "381--396",
editor = "Albert Bifet and Tomas Krilavi{\v c}ius and Ioanna Miliou and Slawomir Nowaczyk",
booktitle = "Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2024, Proceedings",
address = "德国",
}