Optimizing People Sourcing Through Semantic Matching of Job Description Documents and Candidate Profile Using Improved Topic Modelling Techniques

Abstract

People sourcing for a particular job role in any corporate venture is a painstaking, progress-impeding task, especially given the changing job market trends, quality of candidate profile and the sheer number of applicants. This paper elucidates an improvised topic modelling approach, where the semantic analysis stage is prepended with text summarization, for the procedure of people sourcing for job roles using topic modelling and machine learning techniques. The distinction between choosing the perfect candidate for the job and choosing a good candidate who is adept in certain domains but not relevant for the job and getting the candidate up to speed by providing on job training, and the downtime involved in the process is certainly a deciding factor in hiring a potential employee. The following paper aims to alleviate this issue by describing the algorithm which identifies the most suitable candidate in the applicants’ pool through a novel, robust approach which uses topic modelling techniques like Latent Semantic Indexing (LSA) and Latent Dirichlet Allocation (LDA). It is empirically found that the precision of profile matching task was enhanced by using text summarization (through TextRank model) for both LSA and LDA by 21.4% and 50% respectively, and LSA outperforming LDA with regard to precision, in both with and without text summarization cases by 23.8% and 51.57%, respectively.