Design and Implementation of Employment Recommendation Service Platform for College Graduates

Author/s: Liwei Gu, Zhe Zhou, Yulian Zhu

DOI: 10.12738/estp.2018.5.088 

Year: 2018 Vol: 18 Number: 5


In recent years, the university enrolment expansion policy has led to the continuously increasing college graduates. Every year, a large number of graduates flock for massive business recruitment information, while it costs much time and effort to find the suitable jobs in such huge recruitment information. In this context, this paper proposed an employment recommendation platform for college graduates under the background of big data. The platform calculates the similarity index between graduates and recruiting enterprises by the SimRank algorithm and K-Means algorithm successively. Then, the application index was obtained according to the ageRank algorithm. Finally, the similarity index and application index were matched to get the recommended ranking weight of enterprises. In this way, the enterprises ranking among the first can be recommended to corresponding graduates. This platform has been tested and can achieve the initial purpose, which can be used to provide graduates with scientific and rational recommendation services. It is of high practical value as it can improve application successful rate and reduce the time cost for looking a job.

K-Means Algorithm, Recommendation Services, Recommendation Ranking, Simrank Algorithm

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