Job Matching Platform Using Latent Semantic Indexing and Location Mapping Algorithms

Nowadays, there are existing online job matching platform. Though these existing online jobs matching offer increased computation speed of servers and more convenient online job matching, they still strive to improve their job matching applications to achieve accuracy and relevancy. This will further improve the hiring process and reduce the hiring time and cost for companies. However, since most of these job matching methods are still based on the basic and traditional approach, the job qualification information of the job seeker is not effectively compared to the basic job requirements provided by the employer. Thus, when the job seeker uses these traditional online recruitment applications which normally use only simple Boolean operations to compare information, irrelevant jobs would be matched, or a number of job descriptions will be obtained. This research endeavour proposed algorithms to recommend suitable jobs to job seekers based on the collection and analysis of information, and applicants on a specific position to employers using latent semantic indexing and location mapping algorithms. Latent Semantic Indexing algorithm extract and represent the contextual use and meaning of words thru statistical computation applied to document list. The location of jobs input of employers/recruiters are connected to the location mapping module. Likewise, the requested work locations input of job seekers is connected into “geocodes”. Based on the results and analysis of the developed job matching platform, it can discover similar jobs from a query job and employer can easily evaluate candidate job seeker without the needs of human intervention. The testing results show that using latent semantic indexing and location mapping algorithms performed the best in matching the similar jobs.

Keywords – corpus, job matching, job seeker, latent semantic indexing, location mapping.