Trend-Aware Tensor Factorization for Job Skill Demand Analysis

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.