Abstract:
In recent years, Recommender systems are utilized in a variety of areas. One reason behind
why we want a recommender system in current society is that an individual has a large number
of alternatives to use because of the pervasiveness of the Internet. A recommender system seeks
to estimate and predict user content preference. Old recommender systems used State-of-the-art
recommender algorithms like content based filtering to predict ratings. Career Recommender
system provides Engineering candidates the best possible available jobs relevant to their skills,
qualification, etc. Four to six major engineering disciplines are covered in this recommender
system. The proposed approach is tested using a career recommendation dataset which is
collected from many students of different disciplines of various universities. A deep NLP based
CNN model is used to predict the best jobs with maximum precision.512 hidden layers are used
to increase the performance of this system. Career recommendation takes care of the users and
saves their cost and time spending on traditional job searching methods. Comparative study
demonstrations that the proposed methodology of prediction of the best jobs achieves better
results with an accuracy of 84% when matched with content based filtering technique where
81% accuracy is gained for content based career recommender system.
