Towards Personalized Job Recommendations: A Natural Language Processing Perspective
Priyanka Singla, Vishal A. Verma · 2023
Online job portals have rapidly expanded, making it easier for job searchers to find employment. However, it can take much work for job searchers to find the ideal position that matches their skills and preferences due to the abundance of job postings. This paper presents a system for recommending relevant job listings to students using machine learning and natural language processing techniques to solve this issue. The system employs a hybrid strategy to generate precise suggestions, combining collaborative filtering and content-based filtering. First, the system examines the student's resume, specifications, and posting to provide the most pertinent job suggestions. Additionally, the system suggests the top jobs to the user by analyzing and gauging the similarity between the user choice and explicit job listing features. The Recommender System is then evaluated using precision, recall, and Fl score.