A recommender system for employee recruitment

Abstract

With the rapid and constant influx of data in today’s increasing digital world, job seeking has also evolved where applicants can apply for numerous positions with just a few clicks of a button within a few minutes. Coupled with an increasingly mobile world, this has significantly increased competition for jobs, with applicants eyeing both local and international roles. Consequently, Human Resources face an immense challenge in efficiently filtering and evaluating the overwhelming number of resumes they receive. Furthermore, Human Resources may lack the necessary domain knowledge to accurately assess an applicant’s qualifications in highly specialised fields.

To address these challenges, this project explores the development of a recommender system for employee recruitment, focusing on job title prediction based on the resume. Natural Language Processing techniques and Machine Learning models were used on an online dataset to classify resumes into their relevant roles. Models such as Random Forest, Logistic Regression, Support Vector Classifier and k-Nearest Neighbours were implemented and evaluated. Hyperparameter tuning, feature selection and varying dataset size were also done to assess their impact on the model accuracy.

This project demonstrated that Machine Learning models can be an effective approach for job classification across a range of roles. However, incorporating additional factors could further enhance the comprehensiveness of the model’s assessment.