ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

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

Jain, L and Harsha Vardhan, MA and Kathiresan, G and Narayan, A (2021) Optimizing People Sourcing Through Semantic Matching of Job Description Documents and Candidate Profile Using Improved Topic Modelling Techniques. In: International Conference on Artificial Intelligence and Data Engineering, AIDE 2019, 23-24 May 2019, Mangalore; India, pp. 899-908.

[img] PDF
adv_int_sys_com_1133-2021_899-908_2021.pdf - Published Version
Restricted to Registered users only

Download (284kB) | Request a copy
Official URL: https://dx.doi.org/10.1007/978-981-15-3514-7_66


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. © 2021, Springer Nature Singapore Pte Ltd.

Item Type: Conference Paper
Publication: Advances in Intelligent Systems and Computing
Publisher: Springer
Additional Information: The copyright of this article belongs to Springer
Keywords: Artificial intelligence; Employment; Job analysis; Personnel training; Semantics; Statistics; Text processing, Corporate ventures; Latent dirichlet allocations; Latent Semantic Indexing; Machine learning techniques; Modelling techniques; Robust approaches; Semantic matching; Text summarization, Learning systems
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 12 Oct 2020 06:22
Last Modified: 12 Oct 2020 06:22
URI: http://eprints.iisc.ac.in/id/eprint/66582

Actions (login required)

View Item View Item