Leveraging Word Embedding for Automated Candidate Ranking in Talent Acquisition Processes

Abstract

Ranking the applicants who applied for a certain position in a company is mostly done manually. To ease this process, this system creates a ranking system by giving scores for each applicant based on the word embedding model trained using the past datasets. The job advertisements related to information technology fields or related to certain positions are collected and trained a model using the word embedding process. The system compares the resume of the applicant with this model and allocates a specific score for each applicant and orders them in the ascending order. Data crawling and scraping, text preprocessing and training the model are the main components in this research. The goal of this research is to collect the data of job openings related to the information technology industry and collect the job seekers information through the web scraping and crawling and train a model to rank the applicants. The crawled data is used to prepare the corpus. Python scrapy is used to prepare the crawler script for this crawling mechanism. The crawled data is then undergone for the preprocessing. Finally, the preprocessed corpus is undergone for the word embedding. Word2Vec, Gensim are some algorithms used here to train a model. This model is used to compare the resumes of each applicant and get value from the model and finally it will output a total score for each resume and then the system finally ranks the applicants based on the scores they got in ascending order.