Resume Screening and Ranking using Convolutional Neural Network
Sonali Mhatre, Bhawana S. Dakhare, Vaibhav Ankolekar, Neha Chogale, Rutuja Navghane, Pooja Gotarne
Abstract
Manual filtering becomes a tedious task for the recruiter as there are thousands of candidates applying for a single job posting. In this paper, a Long Short-term Memory (LSTM)-based and Convolutional Neural Network (CNN)-based approach are introduced for categorizing resumes submitted by candidates. Along with proposed CNN and LSTM model, also implemented other baseline models such as Random Forest, K-nearest neighbor, and Support Vector Machine. The resumes of the candidates and the job description are compared using Cosine Similarity. The list of job applicants is ranked based on the percentage obtained by Cosine Similarity.
Topics & Concepts
Cosine similarityComputer scienceConvolutional neural networkRanking (information retrieval)Similarity (geometry)Artificial intelligenceTask (project management)Random forestk-nearest neighbors algorithmSupport vector machineMachine learningBaseline (sea)Pattern recognition (psychology)Data miningInformation retrievalImage (mathematics)EconomicsManagementGeologyOceanographyTopic ModelingText and Document Classification TechnologiesCognitive Computing and Networks