article

A hierarchical similarity based job recommendation service framework for university students

Authors:

Yuanxin Ouyang,

Zhang XiongAuthors Info & Claims

Frontiers of Computer Science: Selected Publications from Chinese Universities, Volume 11, Issue 5

Pages 912 - 922

https://doi.org/10.1007/s11704-016-5570-y

Published: 01 October 2017 Publication History

Abstract

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the information overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method's potential.

References

[1]

Paparrizos I, Cambazoglu B B, Gionis A. Machine learned job recommendation. In Proceedings of the 5th ACM Conference on Recommender Systems. 2011, 325---328

Digital Library

[2]

Staton M G. Improving student job placement and assessment through the use of digital marketing certification programs. Marketing Education Review, 2015, 26(1): 20---24

[3]

Renn R W, Robert Steinbauer R, Taylor R, Detwiler D. School-towork transition: Mentor career support and student career planning, job search intentions, and self-defeating job search behavior. Journal of Vocational Behavior, 2014, 85(3): 422---432

[4]

Liu D. Parental involvement and university graduate employment in china. Journal of Education and Work, 2016, 29(1): 98---113

[5]

Lin M C, Ching G S. College students' employability: implications of part-time job during college years. In: Chen H eds. Advances in Public, Environmental and Occupational Health. Singapore Management and Sports Science Institute, 2014, 101---106

[6]

Ellison N B, Steinfield C, Lampe C. The benefits of Facebook "Friends:" social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication, 2007, 12(4): 1143---1168

[7]

Skeels M M, Grudin J. When social networks cross boundaries: a case study of workplace use of Facebook and LinkedIn. In: Proceedings of the 2009 International ACM SIGGROUP Conference on Supporting Group Work. 2009, 95---104

Digital Library

[8]

Breaugh J A. Employee recruitment: current knowledge and important areas for future research. Human Resource Management Review, 2008, 18(3): 103---118

[9]

Obukhova E. Motivation Vs. Relevance: using strong ties to find a job in urban china. Social Science Research, 2012, 41(3): 570---580

[10]

Peterson A. On the prowl: how to hunt and score your first job. Educational Horizons, 2014, 92(3): 13---15

[11]

Al-Otaibi S T, Ykhlef M. A survey of job recommender systems. International Journal of the Physical Sciences, 2012, 7(29): 5127---5142

[12]

Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734---749

Digital Library

[13]

Schafer J B, Konstan J, Riedl J. Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce. 1999, 158---166

Digital Library

[14]

Lu Y, El Helou S, Gillet D. A recommender system for job seeking and recruiting website. In: Proceedings of the 22nd International Conference on World Wide Web. 2013, 963---966

Digital Library

[15]

Fu Y T, Hwang T K, Li Y M, Lin L F. A social referral mechanism for job reference recommendation. In: Proceedings of the 21st Americas Conference on Information Systems. 2015

[16]

Zheng S, Hong W X, Zhang N, Yang F. Job recommender systems: a survey. In Proceedings of the 7th International Conference on Computer Science & Education. 2012, 920---924

[17]

Wang J, Zhang Y, Posse C, Bhasin A. Is it time for a career switch? In: Proceedings of the 22nd International Conference on World Wide Web. 2013, 1377---1388

Digital Library

[18]

Abel F. We know where you should work next summer: job recommendations. In: Proceedings of the 9th ACMConference on Recommender Systems. 2015, 230

Digital Library

[19]

Diaby M, Viennet E, Launay T. Toward the next generation of recruitment tools: an online social network-based job recommender system. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2013, 821---828

Digital Library

[20]

Wu L, Norman I J. An investigation of job satisfaction, organizational commitment and role conflict and ambiguity in a sample of Chinese undergraduate nursing students. Nurse Education Today, 2006, 26(4): 304---314

[21]

Xiao B, Benbasat I. E-commerce product recommendation agents: use, characteristics, and impact. MIS Quarterly, 2007, 31(1): 137---209

Digital Library

[22]

Li Q, Wang J, Chen Y P, Lin Z X. User comments for news recommendation in forum-based social media. Information Sciences, 2010, 180(24): 4929---4939

Digital Library

[23]

Resnick P, Varian H R. Recommender systems. Communications of the ACM, 1997, 40(3): 56---58

Digital Library

[24]

Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACMconference on Computer Supported Cooperative Work, 1994, 175---186

Digital Library

[25]

Ashok B, Joy J, Liang H, Rajamani S K, Srinivasa G, Vangala V. DebugAdvisor: a recommender system for debugging. In: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, 2009, 373---382

Digital Library

[26]

Malinowski J, Keim T, Wendt O, Weitzel T. Matching people and jobs: a bilateral recommendation approach. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences. 2006

Digital Library

[27]

Rafter R, Bradley K, Smyth B. Automated collaborative filtering applications for online recruitment services. In: Proceedings of the 2000 International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. 2000, 363---368

Digital Library

[28]

Miller B N, Albert I, Lam S K, Konstan J A, Riedl J. MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces. 2003, 263---266

Digital Library

[29]

Al-Otaibi S T, Ykhlef M. Job recommendation systems for enhancing e-recruitment process. In: Proceedings of the International Conference on Information and Knowledge Engineering. 2012.

[30]

Hong W X, Zheng S T, Wang H. Dynamic user profile-based job recommender system. In Proceedings of the 8th International Conference on Computer Science & Education. 2013, 1499---1503

[31]

Yi X, Allan J, Croft W B. Matching resumes and jobs based on relevance models. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 809---810

Digital Library

[32]

Almalis N D, Tsihrintzis G A, Karagiannis N. A content based approach for recommending personnel for job positions. In: Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications. 2014, 45---49

[33]

Bastian M, Hayes M, Vaughan W, Shah S, Skomoroch P, Kim H, Uryasev S, Lloyd C. LinkedIn skills: large-scale topic extraction and inference. In: Proceedings of the 8th ACM Conference on Recommender Systems. 2014, 1---8

Digital Library

[34]

Cheng Y, Xie Y S, Chen Z Z, Agrawal A, Choudhary A, Guo S T. Jobminer: a real-time system for mining job-related patterns from social media. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1450---1453

Digital Library

[35]

Zhang Y Y, Yang C, Niu Z X. A research of job recommendation system based on collaborative filtering. In: Proceedings of the 7th International Symposium on Computational Intelligence and Design. 2014, 533---538

Digital Library

[36]

Fazel-Zarandi M, Fox M S. Semantic matchmaking for job recruitment: an ontology-based hybrid approach. In: Proceedings of the 8th International Semantic Web Conference. 2009

[37]

Drigas A, Kouremenos S, Vrettos S, Vrettaros J, Kouremenos D. An expert system for job matching of the unemployed. Expert Systems with Applications, 2004, 26(2): 217---224

[38]

Hong W X, Zheng S T, Wang H, Shi J C. A job recommender system based on user clustering. Journal of Computers, 2013, 8(8): 1960---1967

[39]

Bradley K, Rafter R, Smyth B. Case-based user profiling for content personalisation. In: Proceedings of the 2000 International Conference on Adaptive Hypermedia and AdaptiveWeb-Based Systems. 2000, 62---72

Digital Library

[40]

Chien C F, Chen L F. Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Expert Systems with applications, 2008, 34(1): 280---290

Digital Library

[41]

Pizzato L, Rej T, Chung T, Koprinska I, Kay J. RECON: a reciprocal recommender for online dating. In: Proceedings of the 4th ACM conference on Recommender systems. 2010, 207---214

Digital Library

[42]

Yu H T, Liu C R, Zhang F Z. Reciprocal recommendation algorithm for the field of recruitment. Journal of Information & Computational Science, 2011, 8(16): 4061---4068

[43]

Lee D H, Brusilovsky P. Fighting information overflow with personalized comprehensive information access: a proactive job recommender. In: Proceedings of the 3rd International Conference on Autonomic and Autonomous Systems. 2007, 21

Digital Library

[44]

Keller J M, Gray M R, Givens J A. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man and Cybernetics, 1985, (4): 580---585

[45]

Barber A E, Daly C L, Giannantonio C M, Phillips J M. Job search activities: an examination of changes over time. Personnel Psychology, 1994, 47(4): 739---766

[46]

Teevan J, Dumais S T, Horvitz E. Personalizing search via automated analysis of interests and activities. In: Proceedings of the 28th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval. 2005, 449---456

Digital Library

[47]

Cormack G V, Lynam T R. Statistical precision of information retrieval evaluation. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 533---540

Digital Library

[48]

McLaughlin M R, Herlocker J L. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 329---336

Digital Library

Cited By

Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Zhu HZhao YWu YChen YLi WZheng QTian F(2023)Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College StudentsAdvanced Data Mining and Applications10.1007/978-3-031-46664-9_37(550-564)Online publication date: 27-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-46664-9_37
Xia FGuo TBai XShatte ALiu ZTang J(2022)SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big DataACM/IMS Transactions on Data Science10.1145/35103612:4(1-24)Online publication date: 30-Mar-2022
https://dl.acm.org/doi/10.1145/3510361
Show More Cited By

Recommendations

A Combined Representation Learning Approach for Better Job and Skill Recommendation
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to ...
User click prediction for personalized job recommendation

Major job search engines aggregate tens of millions of job postings online to enable job seekers to find valuable employment opportunities. Predicting the probability that a given user clicks on jobs is crucial to job search engines as the prediction ...
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended. Some items may be recommended much more than others. Recommenders are increasingly used in domains where items ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 11, Issue 5

October 2017

180 pages

ISSN:2095-2228

EISSN:2095-2236

Issue’s Table of Contents

Copyright © Copyright © 2017 Higher Education Press and Springer-Verlag GmbH Germany.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2017

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Zhu HZhao YWu YChen YLi WZheng QTian F(2023)Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College StudentsAdvanced Data Mining and Applications10.1007/978-3-031-46664-9_37(550-564)Online publication date: 27-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-46664-9_37
Xia FGuo TBai XShatte ALiu ZTang J(2022)SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big DataACM/IMS Transactions on Data Science10.1145/35103612:4(1-24)Online publication date: 30-Mar-2022
https://dl.acm.org/doi/10.1145/3510361
Faqihi AMiah S(2022)Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to Extend the TOE FrameworkBig Data Intelligence and Computing10.1007/978-981-99-2233-8_5(69-82)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1007/978-981-99-2233-8_5
Wang CZhu HHao QXiao KXiong H(2021)Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative PerspectiveProceedings of the Web Conference 202110.1145/3442381.3449959(612-623)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449959
Cardoso AMourão FRocha L(2021)The matching scarcity problemExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114764175:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114764
Cardoso AMourão FRocha Lde Salles Soares Neto C(2020)Mitigating Matching Scarcity in Recruitment Recommendation DomainsProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430968(165-172)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3430968
Palomares IHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Reciprocal Recommendation: Matching Users with the Right UsersProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401420(2429-2431)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401420
Freire Mde Castro L(2020)e-Recruitment recommender systems: a systematic reviewKnowledge and Information Systems10.1007/s10115-020-01522-863:1(1-20)Online publication date: 5-Nov-2020
https://dl.acm.org/doi/10.1007/s10115-020-01522-8
Alkan ODaly EBotea AValente APedemonte PFu WPan SBrdiczka OChau PCalvary G(2019)Where can my career take me?Proceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302311(603-613)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3302311
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents