Volume 8 Number 8 (Aug. 2013)
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JCP 2013 Vol.8(8): 1960-1967 ISSN: 1796-203X
doi: 10.4304/jcp.8.8.1960-1967

A Job Recommender System Based on User Clustering

Wenxing Hong1, Siting Zheng1, Huan Wang1, and Jianchao Shi2
1 School of Information Science and Technology, Xiamen University, Xiamen, China
2 Department of Computer Science, Westsouth Petrolum University, Chengdu, China


Abstract—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.

Index Terms—online job recommender system, user cluster, recommendation approach

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Cite: Wenxing Hong, Siting Zheng, Huan Wang, and Jianchao Shi, " A Job Recommender System Based on User Clustering," Journal of Computers vol. 8, no. 8, pp. 1960-1967, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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