Machine Learned Resume-Job Matching Solution
This addresses the frustrating search experience for job seekers and employers in online matching engines, though it appears incremental as it builds on existing machine learning techniques.
The authors tackled the problem of inefficient resume-job matching in online engines by developing a machine learning solution with unsupervised feature extraction, base classifiers, and ensemble methods, which significantly improved prediction precision for position, salary, educational background, and company scale on a dataset of over 47,000 resumes.
Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.