{"type":"ScholarlyArticle","title":"Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective","authors":[{"name":"Huang Xu","slug":"huang-xu","given":"Huang","family":"Xu","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , 710129 China"},{"name":"Zhiwen Yu","slug":"zhiwen-yu","given":"Zhiwen","family":"Yu","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , 710129 China"},{"name":"Bin Guo","slug":"bin-guo","given":"Bin","family":"Guo","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , 710129 China"},{"name":"Mingfei Teng","slug":"mingfei-teng","given":"Mingfei","family":"Teng","affiliation":"Rutgers University"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Rutgers University"}],"abstract":"A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed  for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.","doi":"10.24963/ijcai.2018/500","url":"https://ijcai.org/proceedings/2018/500","pdf":"https://ijcai.org/proceedings/2018/0500.pdf","markdown":"https://ijcai.org/proceedings/2018/500.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/500","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":500,"pages":{"first":3599,"last":3605},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Data Mining","Uncertainty in AI: Bayesian Networks","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Bekkerman and Gavish, 2011] Ron Bekkerman and Matan Gavish. 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