{"type":"ScholarlyArticle","title":"A Joint Learning Approach to Intelligent Job Interview Assessment","authors":[{"name":"Dazhong Shen","slug":"dazhong-shen","given":"Dazhong","family":"Shen","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of S&T of China ,; Baidu Talent Intelligence Center ,"},{"name":"Hengshu Zhu","slug":"hengshu-zhu","given":"Hengshu","family":"Zhu","affiliation":"Baidu Talent Intelligence Center ,"},{"name":"Chen Zhu","slug":"chen-zhu","given":"Chen","family":"Zhu","affiliation":"Baidu Talent Intelligence Center ,"},{"name":"Tong Xu","slug":"tong-xu","given":"Tong","family":"Xu","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of S&T of China ,; Baidu Talent Intelligence Center ,"},{"name":"Chao Ma","slug":"chao-ma","given":"Chao","family":"Ma","affiliation":"Baidu Talent Intelligence Center ,"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of S&T of China ,; Baidu Talent Intelligence Center ,; Business Intelligence Lab , Baidu Research ,; National Engineering Laboratory of Deep Learning Technology and Application , China."}],"abstract":"The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data. Specifically, we develop a latent variable model named Joint Learning Model on Interview Assessment (JLMIA) to jointly model job description, candidate resume and interview assessment. JLMIA can effectively learn the representative perspectives of different job interview processes from the successful job application records in history. Therefore, a variety of applications in job interviews can be enabled, such as person-job fit and interview question recommendation. Extensive experiments conducted on real-world data clearly validate the effectiveness of JLMIA, which can lead to substantially less bias in job interviews and provide a valuable understanding of job interview assessment.","doi":"10.24963/ijcai.2018/492","url":"https://ijcai.org/proceedings/2018/492","pdf":"https://ijcai.org/proceedings/2018/0492.pdf","markdown":"https://ijcai.org/proceedings/2018/492.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/492","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":492,"pages":{"first":3542,"last":3548},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Machine Learning Applications: Other Applications","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bersin, 2013] Josh Bersin. https://www.forbes.com/sites/ joshbersin/2013/05/23/corporate-recruitment- transformed-new-breed-of-service-providers/. 2013.","title":"Josh Bersin","authors":"Bersin","year":"2013","venue":null,"doi":null,"url":"https://www.forbes.com/sites/joshbersin/2013/05/23/corporate-recruitment-transformed-new-breed-of-service-providers/.2013","internal_url":null},{"position":2,"raw":"[Blei et al., 2003] David M. 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