Job Recommendation System Using NLP

Abstract

Abstract: Recommender systems is one of the most successful machine learning applications. We are exposed to the products/information recommended by these systems everywhere in our daily life. This project will introduce several recommender systems in NLP, specifically in the domain of online recruitment. I will explain the important ideas behind the recommender systems in our web application, it might be also similar with those in recruiting platforms like LinkedIn, Xing. One of my main jobs is to design and develop recommender systems on the skill-based matching app. Before getting into this specific topic, I would like to go over the general concepts and methods of current recommender system in different domains. We have provided a brief overview of the approaches, techniques, and applications of recommendation systems in this paper. One significant application of recommendation systems is in the field of job recruitment, where candidates are chosen using online job recruitment portals based on their profiles, employment histories, and behavioral tendencies. According to a recent survey, this field has not been thoroughly investigated up to this point, and the current job recommender system has many flaws. They analyze resumes, profiles, and job descriptions, but because of the "cold start" problem, new job postings and candidate profiles are not properlymatched. In some cases, potential candidates lose their jobs as a result of inadequate job descriptions and education information in the ontology.