New Frontiers in Communication and Intelligent Systems
A Machine Learning Approach for Personalized Job Recommendation System
Authors: Harini Priya B R, Kannimalar K, Saranya S B and Subbulakshmi B
Publishing Date: 06-05-2022
ISBN: 978-81-95502-00-4
Abstract
Every year lakhs and lakhs of students are graduating. The students from top colleges find a suitable job with fewer struggles since most of the companies visits their college for recruitment. But this is not the same case for all colleges. So these students go in search of search engines to find a suitable job. This is not an easy task. Thus a recommendation engine that would recommend jobs based on their search history is built. Currently, more websites give plenty of information about job opportunities that may not intersect. The goal is to ease the work of job searchers by providing them with personalized job recommendations from various websites. This approach tries to overcome the drawbacks of existing papers such as cold start problems, security issues and scalability thus providing a better recommendation system. Thus the recommender system can play a significant role to help college graduates and job seekers to fulfill their dreams by recommending a job based on their interests.
Keywords
Recommendation engine, Machine Learning, Hybrid recommendation.
Cite as
Harini Priya B R, Kannimalar K, Saranya S B and Subbulakshmi B, "A Machine Learning Approach for Personalized Job Recommendation System", In: Rahul Srivastava and Aditya Kr. Singh Pundir (eds), New Frontiers in Communication and Intelligent Systems, SCRS, India, 2022, pp. 399-404. https://doi.org/10.52458/978-81-95502-00-4-42