Resume Analyser: Automated Resume Ranking Software

Asst Prof. Jyothis Joseph
2020 International Journal for Research in Applied Science and Engineering Technology  
Recruiting candidates to fit a particular job profile is a task crucial to most of the companies. Due to increasing growth in online recruitment, traditional hiring methods are becoming inefficient. The conventional techniques usually include a laborintensive process of manually searching through the applied candidates, reviewing their resumes, and then producing a shortlist of suitable candidates to be interviewed. In this era of technology, job searching has become smarter and more accessible
more » ... and more accessible at the same time. The companies receive enormous numbers of resumes/CVs, which are not always structured. There have been lots of work done for the job searching process. Whereas, the process of selecting a candidate based on their resume has not been entirely automated. Index Terms: resume, job, , recruitment I. INTRODUCTION To hire the right person at the right time, recruiters must be able to screen resumes correctly. Resume screening is the process of determining whether a candidate is qualified for a role based his or her education, experience, and other information captured on their resume. The essence of any good recruitment strategy lies in efficient and effective resume screening. The objective of resume screening is to locate the most qualified candidates for a job.In the present system the candidate has to fill each and every information regarding their resume in a manual form which takes large amount of time and then also the candidates, are not satisfied by the job which the present system prefer according to there skills. Our system will act as a handshake between two entities. i.e. the company who prefer the best possible candidate and the candidate who prefers the best possible job according to his or her skills and ability. Our system is an automated resume screening software using NLP and machine learning. This AI powered resume screening software goes beyond keywords and screens resumes contextually. After resume screening, the software ranks candidates based on the recruiters job requirements in real-time. This ranking is relative. The software uses natural language processing and machine learning for matching and ranking candidates in real time. II. LITERATURE SURVEY A. Learning to Rank Resumes Recruiting and assigning right candidate for right job con-sumes significant time and efforts in an organization. Auto-mated resume information retrieval systems are increasingly looked upon as the solution to this problem. In this paper, we focus on problem of learning an end-user specific ranking in such a resume search engine. We provide experimental results of SVM rank algorithm for this task. B. Resume Ranking using NLP and ML Using NLP(Natural Language Processing) and ML(Machine Learning) to rank the resumes according to the given con-straint, this intelligent system ranks the resume of any format according to the given constraints or the following requirement provided by the client company. We will basically take the bulk of input resume from the client company and that client company will also provide the requirement and the constraints according to which the resume should be ranked by our system. Beside the information provide by the resume we are going to read the candidates social profiles (like LinkedIn, Github etc) which will give us the more genuine information about that candidate. C. ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screen-ing to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to de-velop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company.
doi:10.22214/ijraset.2020.30378 fatcat:7e6vywawo5bfffy2lmfrnbjkg4