Applying Data Mining for Job Recommendations by Exploring Job Preferences

Abstract

Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that use different strategies, here efforts have been put to make the job recommendations on the basis of candidate‟s profile matching as well as preserving candidate‟s job behavior or preferences. Firstly, the rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidate‟s own applied job history. Through this technique, a significant level of accuracy, around eighty percent, has been achieved over other basic methods of job recommendations.