Intelligent Job Matching with Self-learning Recommendation Engine

Abstract

Most of the job matching engine today only takes into consideration with the information directly extracted from the resumes and vacancies. The two set of information match against each other based on some predefined rules. This paper presents the methodologies to perform job matching with user provided information, from now on referred to as parameters. A few common parameters for job matching includes domain of job, job title, position, knowledge, experience, location, salary and etc. Predefined rules assigning weighting factor to each parameter and defining how matching results could be filter and rank to produce job matching result. Besides, this paper also explained the methodologies used to auto-filling in places where a candidate has missed out certain important information in their resume. The auto-filing utilized the self-learning engine to collect information, analysis the data and auto generates standard template for different categories group. The standard template is categorized based on some parameters such as qualification, education background and job experience. The self-learning engine uses the advantage of ontology to make inference from data in order to discover missing parameters as well as new relationship among the parameters. The inference techniques also improve the possible inconsistencies of various parameters. The system then performs a final job matching based on direct parameters extracted from user input and dynamically populated parameters from matched standard template.