Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Shuqing Bian, Chen Xu, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang Song, Tao Zhang, Ji-Rong Wen
Abstract
With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms.
Topics & Concepts
Computer scienceTask (project management)Matching (statistics)Artificial intelligenceMachine learningLabeled dataStatisticsMathematicsManagementEconomicsRecommender Systems and TechniquesMultimodal Machine Learning ApplicationsAdvanced Graph Neural Networks