A Hybrid Job Recommendation Algorithm for Intelligent Employment System Using User Profile-Based Filtering

Abstract

With high speed of Internet development, especially in area of college employment system (CES), which are used to searching the Internet for these college graduates to acquire the proper positions of the proper company. Meanwhile, how to find the right candidate from the massive graduate-ability data matrix becomes a hard issue. Traditional campus recruitment includes so many steps which will cost much time and human capital. Thus, our proposed hybrid talent recommendation method can solve this problem by predicting the future proper candidate for the company by comparing the ability data of the present company talent with recent campus graduate who do not have any working experience. Ability similarity and demographic similarity are both considered in our method combined with traditional collaborative filtering making our prediction more precise and suitable for College Employment Systems in real situation.