Machine-learning based big textual data analysis for job candidate resources extraction and matching

点击翻译按钮获取中文摘要

The rapid growth of Internet recruiting has fostered a huge number of resumes and related documents in various digital formats. To make sense of this big data, this project will develop information services from highly accurate models that combines power of machine learning and natural language processing, which can discover useful patterns for automatically matching candidates to jobs based on their profiles/resumes, and predicting future resource availability. This will be done by creating resource prediction models from various textual data sources and developing a service that can be integrated into the partner company's existing solutions. The proposed approach presents a cost-effective and efficient solution to extract and use resource information from large unstructured text data, by taking advantage of current ML and natural language processing technology. Candidates for certain positions can be automatically identified and recommended to potential industries by extracting patterns from resumes. The research outcomes will allow automated sourcing and screening of candidates for the partner company's expanding small business and will enable the company to grow and support its local employment markets as well as benefit the employment markets beyond local areas within 3 to 5 years.