Skip to main content
SpringerLink
Log in

A Content-Based Recommendation Approach Using Semantic User Profile in E-recruitment

  • Conference paper
  • First Online:
Theory and Practice of Natural Computing (TPNC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9477))

Included in the following conference series:

  • 538 Accesses

Abstract

In this paper, we propose a content-based recommendation approach in the domain of e-recruitment to recommend users with job offers that suit the most their profile and learned preferences. In order to present the best offers, we construct a semantic vocabulary of the domain from the job offers corpus and initialize a profile for each user based on his Curriculum Vitae. Our method is enriching the user profiles using triggers and statistical methods following his actions regarding the job offers. The approach we propose presents to the users job offers that are the closest to their learned needs and interests which also can be updated based on his daily actions regarding these offers.

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 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
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

Notes

  1. 1.

    http://www.eremedia.com/ere/recruitment-5-0-the-future-of-recruiting-the-final-chapter/.

  2. 2.

    http://www.emploitic.com.

References

  1. Adomavicius, G., Zhang, J.: Stability of recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 30(4), 23 (2012)

    Article  Google Scholar 

  2. Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)

    Article  Google Scholar 

  3. Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)

    Google Scholar 

  4. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52, Morgan Kaufmann Publishers Inc (1998)

    Google Scholar 

  5. Burke, R.: Integrating knowledge-based and collaborative-filtering recommender systems. In: Proceedings of the Workshop on AI and Electronic Commerce, pp. 69–72 (1999)

    Google Scholar 

  6. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the fifth ACM Conference on Recommender Systems, pp. 197–204, ACM (2011)

    Google Scholar 

  8. Lavecchia, C., Smaili, K., Langlois, D., Haton, J.P.: Using inter-lingual triggers for machine translation. In: 8th Annual Conference of the International Speech Communication Association-INTERSPEECH 2007, pp. 2829–2832, ISCA (2007)

    Google Scholar 

  9. Lee, D.H., Brusilovsky, P.: Fighting information overflow with personalized comprehensive information access: a proactive job recommender. In: Third International Conference on Autonomic and Autonomous Systems 2007, ICAS07, pp. 21–21, IEEE (2007)

    Google Scholar 

  10. Rafter, R., Smyth, B.: Passive profiling from server logs in an online recruitment environment (2001)

    Google Scholar 

  11. Rahutomo, F., Kitasuka, T., Aritsugi, M.: Semantic cosine similarity (2012)

    Google Scholar 

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

    Article  Google Scholar 

  13. Roberts, S.: Control chart tests based on geometric moving averages. Technometrics 1(3), 239–250 (1959)

    Article  Google Scholar 

  14. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)

    MATH  Google Scholar 

  15. Singh, A., Rose, C., Visweswariah, K., Chenthamarakshan, V., Kambhatla, N.: Prospect: a system for screening candidates for recruitment. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 659–668, ACM (2010)

    Google Scholar 

  16. Zakaria, C., Curé, O., Salzano, G., Smaïli, K.: Formalized conflicts detection based on the analysis of multiple emails: an approach combining statistics and ontologies. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2009, Part I. LNCS, vol. 5870, pp. 94–111. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Zibran, M.F.: Chi-squared test of independence. Department of Computer Science, University of Calgary, Alberta, Canada (2007). Accessed 12 Aug 2010

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, El Harrach, Algiers, Algeria

    Oualid Chenni & Yanis Bouda

  2. Laboratoire de Recherche En Intelligence Articifielle (LRIA), USTHB, El Alia BP 32, Bab Ezzouar, Algiers, Algeria

    Hamid Benachour

  3. Laboratoire de Méthodes de Conception des Systèmes (LMCS), Ecole Nationale Suprieure d’Informatique, 16309, El Harrach, Algiers, Algeria

    Chahnez Zakaria

Authors
  1. Oualid Chenni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanis Bouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Benachour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chahnez Zakaria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chahnez Zakaria .

Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Tarragona, Spain

    Adrian-Horia Dediu

  2. European Centre for Soft Computing, Asturias, Spain

    Luis Magdalena

  3. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Tarragona, Spain

    Carlos Martín-Vide

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chenni, O., Bouda, Y., Benachour, H., Zakaria, C. (2015). A Content-Based Recommendation Approach Using Semantic User Profile in E-recruitment. In: Dediu, AH., Magdalena, L., Martín-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2015. Lecture Notes in Computer Science(), vol 9477. Springer, Cham. https://doi.org/10.1007/978-3-319-26841-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26841-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26840-8

  • Online ISBN: 978-3-319-26841-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation