{"type":"ScholarlyArticle","title":"Trend-Aware Tensor Factorization for Job Skill Demand Analysis","authors":[{"name":"Xunxian Wu","slug":"xunxian-wu","given":"Xunxian","family":"Wu","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , School of Computer Science , University of Science and Technology of China"},{"name":"Tong Xu","slug":"tong-xu","given":"Tong","family":"Xu","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , School of Computer Science , University of Science and Technology of China"},{"name":"Hengshu Zhu","slug":"hengshu-zhu","given":"Hengshu","family":"Zhu","affiliation":"Baidu Talent Intelligence Center , Baidu Inc."},{"name":"Le Zhang","slug":"le-zhang","given":"Le","family":"Zhang","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , School of Computer Science , University of Science and Technology of China"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , School of Computer Science , University of Science and Technology of China"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , School of Computer Science , University of Science and Technology of China; Baidu Talent Intelligence Center , Baidu Inc.; Business Intelligence Lab , Baidu Research"}],"abstract":"Given a job position, how to identify the right job skill demand and its evolving trend becomes critically important for both job seekers and employers in the fast-paced job market. Along this line, there still exist various challenges due to the lack of holistic understanding on skills related factors, e.g., the dynamic validity periods of skill trend, as well as the constraints from overlapped business and skill co-occurrence. To address these challenges, in this paper, we propose a trend-aware approach for fine-grained skill demand analysis. Specifically, we first construct a tensor for each timestamp based on the large-scale recruitment data, and then reveal the aggregations among companies and skills by heuristic solutions. Afterwards, the Trend-Aware Tensor Factorization (TATF) framework is designed by integrating multiple confounding factors, i.e., aggregation-based and temporal constraints, to provide more fine-grained representation and evolving trend of job demand for specific job positions. Finally, validations on large-scale real-world data clearly validate the effectiveness of our approach for skill demand analysis.","doi":"10.24963/ijcai.2019/540","url":"https://ijcai.org/proceedings/2019/540","pdf":"https://ijcai.org/proceedings/2019/0540.pdf","markdown":"https://ijcai.org/proceedings/2019/540.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/540","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":540,"pages":{"first":3891,"last":3897},"keywords":["Machine Learning: Data Mining","Machine Learning Applications: Other Applications"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. 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