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2016 | OriginalPaper | Buchkapitel

Rating Prediction Based Job Recommendation Service for College Students

verfasst von : Rui Liu, Yuanxin Ouyang, Wenge Rong, Xin Song, Cui Tang, Zhang Xiong

Erschienen in: Computational Science and Its Applications – ICCSA 2016

Verlag: Springer International Publishing

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Abstract

When college students enter the job market, one of the main difficulties is that they do not have much working experience. To help students find proper jobs, appropriate recommendation systems are becoming a necessity. However, since most students start to find jobs in a very short time, it is difficult for a recommender system due to the lack of history information. To solve this problem, in this research we proposed a rating prediction mechanism by considering the feedback from graduates who have offers and also provided ratings to the employers. By calculating the similarity between the students, a rating prediction method is proposed to generate a list of potential employers for the students. Furthermore, we also take into account the factor of student’s interest into the recommendation list’s generation to further polish the overall performance. Experimental study on real recruitment dataset has shown the model’s potential.

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Vorheriges Kapitel A Semantic Comparison of Clustering Algorithms for the Evaluation of Web-Based Similarity Measures
Nächstes Kapitel Development of M-Health Software for People with Disabilities
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Metadaten
Titel
Rating Prediction Based Job Recommendation Service for College Students
verfasst von
Rui Liu
Yuanxin Ouyang
Wenge Rong
Xin Song
Cui Tang
Zhang Xiong
Copyright-Jahr
2016
Buch
Computational Science and Its Applications – ICCSA 2016

Print ISBN: 978-3-319-42091-2

Electronic ISBN: 978-3-319-42092-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42092-9_35