Dynamic user profile-based job recommender system
Wenxing Hong, Zheng Siting, Huan Wang · 2013
In this paper, we propose a dynamic user profile-based job recommender system. To address the challenge that the job applicants do not update the user profile in a timely manner, we update and extend the user profile dynamically based on the historical applied jobs and behaviors of job applicants. In particular, the statistical results of basic features in the applied jobs are used to update the job applicants'. In addition, feature selection is employed in the text information of jobs that applied by the job applicant for extending the feature. Then a hybrid recommendation algorithm is employed according to the characteristics of user profiles for achieving the dynamic recommendation.