Abstract:
The task of finding the best job candidates among a set of applicants is both time and resource consuming, especially when there are lots of applications. In this concern, the development of a decision support system represents a promising solution to support recruiters and facilitate their job. In this paper, we present an intelligent decision support system named I-Recruiter, that ranks applicants according to the semantic similarity between their resumes and job descriptions; the ranking process is based on machine learning and natural language processing techniques. I-Recruiter is composed of three sequentially connected blocks namely 1) Training block: which is responsible for training the model from a set of resumes, 2) Matching block: that is responsible for matching the resumes to the corresponding job description, and 3) Extracting block: that is responsible for extracting the top n ranked candidates. Experimental results for accuracy and performance showed that I-recruiter is capable of doing the job with high confidence and excellent performance.
