Rating prediction based job recommendation service for college students

Rui Liu; Yuanxin Ouyang; Wenge Rong; Xin Song; Cui Tang; Zhang Xiong

doi:10.1007/978-3-319-42092-9_35

Rating prediction based job recommendation service for college students

Rui Liu
, Yuanxin Ouyang^*
, Wenge Rong
, Xin Song
, Cui Tang
, Zhang Xiong

^*Corresponding author for this work

School of Computer Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

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.

Original language	English
Title of host publication	Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings
Editors	Sanjay Misra, Osvaldo Gervasi, Carmelo M. Torre, David Taniar, Elena Stankova, Ana Maria A.C. Rocha, Shangguang Wang, Beniamino Murgante, Bernady O. Apduhan
Publisher	Springer Verlag
Pages	453-467
Number of pages	15
ISBN (Print)	9783319420912
DOIs	https://doi.org/10.1007/978-3-319-42092-9_35
State	Published - 2016
Event	16th International Conference on Computational Science and Its Applications, ICCSA 2016 - Beijing, China Duration: 4 Jul 2016 → 7 Jul 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9790
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Computational Science and Its Applications, ICCSA 2016
Country/Territory	China
City	Beijing
Period	4/07/16 → 7/07/16

Keywords

Job recommendation
Prediction
Rating
Student profile

Access to Document

10.1007/978-3-319-42092-9_35

Cite this

Liu, R., Ouyang, Y., Rong, W., Song, X., Tang, C., & Xiong, Z. (2016). Rating prediction based job recommendation service for college students. In S. Misra, O. Gervasi, C. M. Torre, D. Taniar, E. Stankova, A. M. A. C. Rocha, S. Wang, B. Murgante, & B. O. Apduhan (Eds.), Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings (pp. 453-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9790). Springer Verlag. https://doi.org/10.1007/978-3-319-42092-9_35

Liu, Rui ; Ouyang, Yuanxin ; Rong, Wenge et al. / Rating prediction based job recommendation service for college students. Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings. editor / Sanjay Misra ; Osvaldo Gervasi ; Carmelo M. Torre ; David Taniar ; Elena Stankova ; Ana Maria A.C. Rocha ; Shangguang Wang ; Beniamino Murgante ; Bernady O. Apduhan. Springer Verlag, 2016. pp. 453-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{15d5fb5a7dab4467b4166a58ea2f1e57,

title = "Rating prediction based job recommendation service for college students",

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{\textquoteright}s interest into the recommendation list{\textquoteright}s generation to further polish the overall performance. Experimental study on real recruitment dataset has shown the model{\textquoteright}s potential.",

keywords = "Job recommendation, Prediction, Rating, Student profile",

author = "Rui Liu and Yuanxin Ouyang and Wenge Rong and Xin Song and Cui Tang and Zhang Xiong",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 16th International Conference on Computational Science and Its Applications, ICCSA 2016 ; Conference date: 04-07-2016 Through 07-07-2016",

year = "2016",

doi = "10.1007/978-3-319-42092-9\_35",

language = "英语",

isbn = "9783319420912",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "453--467",

editor = "Sanjay Misra and Osvaldo Gervasi and Torre, \{Carmelo M.\} and David Taniar and Elena Stankova and Rocha, \{Ana Maria A.C.\} and Shangguang Wang and Beniamino Murgante and Apduhan, \{Bernady O.\}",

booktitle = "Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings",

address = "德国",

}

Liu, R , Ouyang, Y , Rong, W, Song, X, Tang, C & Xiong, Z 2016, Rating prediction based job recommendation service for college students. in S Misra, O Gervasi, CM Torre, D Taniar, E Stankova, AMAC Rocha, S Wang, B Murgante & BO Apduhan (eds), Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9790, Springer Verlag, pp. 453-467, 16th International Conference on Computational Science and Its Applications, ICCSA 2016, Beijing, China, 4/07/16. https://doi.org/10.1007/978-3-319-42092-9_35

Rating prediction based job recommendation service for college students. / Liu, Rui ; Ouyang, Yuanxin ; Rong, Wenge et al.
Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings. ed. / Sanjay Misra; Osvaldo Gervasi; Carmelo M. Torre; David Taniar; Elena Stankova; Ana Maria A.C. Rocha; Shangguang Wang; Beniamino Murgante; Bernady O. Apduhan. Springer Verlag, 2016. p. 453-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9790).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Rating prediction based job recommendation service for college students

AU - Liu, Rui

AU - Ouyang, Yuanxin

AU - Rong, Wenge

AU - Song, Xin

AU - Tang, Cui

AU - Xiong, Zhang

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Job recommendation

KW - Prediction

KW - Rating

KW - Student profile

UR - https://www.scopus.com/pages/publications/84978224684

U2 - 10.1007/978-3-319-42092-9_35

DO - 10.1007/978-3-319-42092-9_35

M3 - 会议稿件

AN - SCOPUS:84978224684

SN - 9783319420912

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 453

EP - 467

BT - Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings

A2 - Misra, Sanjay

A2 - Gervasi, Osvaldo

A2 - Torre, Carmelo M.

A2 - Taniar, David

A2 - Stankova, Elena

A2 - Rocha, Ana Maria A.C.

A2 - Wang, Shangguang

A2 - Murgante, Beniamino

A2 - Apduhan, Bernady O.

PB - Springer Verlag

T2 - 16th International Conference on Computational Science and Its Applications, ICCSA 2016

Y2 - 4 July 2016 through 7 July 2016

ER -

Liu R , Ouyang Y , Rong W, Song X, Tang C, Xiong Z. Rating prediction based job recommendation service for college students. In Misra S, Gervasi O, Torre CM, Taniar D, Stankova E, Rocha AMAC, Wang S, Murgante B, Apduhan BO, editors, Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings. Springer Verlag. 2016. p. 453-467. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42092-9_35

Rating prediction based job recommendation service for college students

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this