A Job Recommender System Based on User Clustering

In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback.  In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main challenge lies on the design of recommendation strategies since different job applicants may have different characteristics. To address the aforementioned challenge, we develop an online JRS, iHR, which groups users into different clusters and employs different recommendation approaches for different user clusters.  As a result, iHR has the capability of choosing the appropriate recommendation approaches according to users’ characteristics. Empirical results demonstrate the effectiveness of the proposed system.