Skip to main content
SpringerLink
Log in

Rating Prediction Based Job Recommendation Service for College Students

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9790))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Al-Otaibi, S.T., Ykhlef, M.: Job recommendation systems for enhancing e-recruitment process. In: Proceedings of 2012 Information and Knowledge Engineering conference (2012)

    Google Scholar 

  3. 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, pp. 45–49 (2014)

    Google Scholar 

  4. 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, pp. 1–8 (2014)

    Google Scholar 

  5. Breaugh, J.A.: Employee recruitment: current knowledge and important areas for future research. Hum. Resour. Manag. Rev. 18(3), 103–118 (2008)

    Article  Google Scholar 

  6. Cheng, Y., Xie, Y., Chen, Z., Agrawal, A., Choudhary, A., Guo, S.: 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, pp. 1450–1453 (2013)

    Google Scholar 

  7. 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, pp. 533–540 (2006)

    Google Scholar 

  8. Drigas, A., Kouremenos, S., Vrettos, S., Vrettaros, J., Kouremenos, D.: An expert system for job matching of the unemployed. Expert Syst. Appl. 26(2), 217–224 (2004)

    Article  Google Scholar 

  9. Ellison, N.B., Steinfield, C., Lampe, C.: The benefits of Facebook “friends:” social capital and college students’ use of online social network sites. J. Comput.-Mediat. Commun. 12(4), 1143–1168 (2007)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002)

    Article  Google Scholar 

  12. Hong, W., Zheng, S., Wang, H.: Dynamic user profile-based job recommender system. In: Proceedings of 8th International Conference onComputer Science and Education, pp. 1499–1503 (2013)

    Google Scholar 

  13. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Miningk, pp. 426–434 (2008)

    Google Scholar 

  14. Lin, M.C., Ching, G.S.: College student’ employability: implications of part-time job during college years. In: Advances in Public, Environmental and Occupational Health, pp. 101–106 (2014)

    Google Scholar 

  15. Liu, D.: Parental involvement and university graduate employment in China. J. Educ. Work 29(1), 98–113 (2016)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. 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, pp. 329–336 (2004)

    Google Scholar 

  18. 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 2003 International Conference on Intelligent User Interfaces, pp. 263–266 (2003)

    Google Scholar 

  19. Obukhova, E.: Motivation vs. relevance: using strong ties to find a job in urban China. Soc. Sci. Res. 41(3), 570–580 (2012)

    Article  Google Scholar 

  20. Paparrizos, I.K., Cambazoglu, B.B., Gionis, A.: Machine learned job recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems, pp. 325–328 (2011)

    Google Scholar 

  21. Peterson, A.: On the prowl: how to hunt and score your first job. Educ. Horiz. 92(3), 13–15 (2014)

    Google Scholar 

  22. 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, pp. 207–214 (2010)

    Google Scholar 

  23. Rafter, R., Bradley, K., Smyth, B.: Automated collaborative filtering applications for online recruitment services. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) AH 2000. LNCS, vol. 1892, pp. 363–368. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  24. Renn, R.W., Steinbauer, R., Taylor, R., Detwiler, D.: School-to-work transition: mentor career support and student career planning, job search intentions, and self-defeating job search behavior. J. Vocat. Behav. 85(3), 422–432 (2014)

    Article  Google Scholar 

  25. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  26. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Proceedings of 5th International Conference on Computer and Information Science, pp. 27–28 (2002)

    Google Scholar 

  27. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260 (2002)

    Google Scholar 

  28. 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, pp. 95–104 (2009)

    Google Scholar 

  29. Staton, M.G.: Improving student job placement and assessment through the use of digital marketing certification programs. Mark. Educ. Rev. 26(1), 20–24 (2015)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  31. Yu, H., Liu, C., Zhang, F.: Reciprocal recommendation algorithm for the field of recruitment. J. Inf. Comput. Sci. 8(16), 4061–4068 (2011)

    Google Scholar 

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

    Google Scholar 

  33. Zheng, S., Hong, W., Zhang, N., Yang, F.: Job recommender systems: a survey. In: Proceedings of 7th International Conference on Computer Science and Education, pp. 920–924 (2012)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National High Technology Research and Development Program of China (No. 2013AA01A601), the National Natural Science Foundation of China (No. 61472021), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. Engineering Research Center of Advanced Computer Application Technology, Ministry of Education, Beihang University, Beijing, China

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

  2. School of Computer Science and Engineering, Beihang University, Beijing, China

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

Authors
  1. Rui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanxin Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenge Rong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuanxin Ouyang .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, R., Ouyang, Y., Rong, W., Song, X., Tang, C., Xiong, Z. (2016). Rating Prediction Based Job Recommendation Service for College Students. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_35

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42091-2

  • Online ISBN: 978-3-319-42092-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation