Skip to main content
SpringerLink
Log in

Hybrid immunizing solution for job recommender system

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Färber F, Weitzel T, Keim T. An automated recommendation approach to selection in personnel recruitment. In: Proceedings of Americas Conference on Information Systems. 2003

    Google Scholar 

  3. 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

    Google Scholar 

  4. Singh A, Catherine R, Visweswariah K. PROSPECT: a system for screening candidates for recruitment. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, 659–668

    Google Scholar 

  5. Al-Otaibi S T, Ykhlef M. An artificial immune system for job recommendation. In: Proceedings of the 3rd IEEE International Conference and Workshop on Bioinspired Intelligence. 2014, 37–43

    Google Scholar 

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

    Google Scholar 

  7. Eiben A, Smith J. What is an evolutionary algorithm. In: Proceedings of Introduction to Evolutionary Computing. 2007

    Google Scholar 

  8. Ujjin S, Bentley P. Particle swarm optimization recommender system. In: Proceedings of the IEEE Swarm Intelligence Symposium. 2003, 124–131

    Google Scholar 

  9. Talbi E G. Metaheuristics from Design to Implementation. Hoboken, NJ: John Wiley & Sons, 2011

    MATH  Google Scholar 

  10. Morrison T, Aickelin U. An artificial immune system as a recommender system for web sites. In: Proceedings of the 1st International Conference on Artificial Immune Systems. 2002, 161–169

    Google Scholar 

  11. Mihaljevi B, Avrak I, Žagar M. An artificial immune system approach to news article recommendation. In: Proceedings of the 27th International Conference on Information Technology Interfaces. 2005

    Google Scholar 

  12. 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 conference. 2012

    Google Scholar 

  13. Paparrizos I, Cambazoglu B, Gionis A. Machine learned job recommendation. In: Proceedings of The 5th ACM Conference On Recommender Systems. 2011, 325–328

    Google Scholar 

  14. 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

    Google Scholar 

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

    Google Scholar 

  16. 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

    Chapter  Google Scholar 

  17. Brusilovsky P. Adaptive hypermedia. User Modeling and User Adapted Interaction, 2001, 11(1–2): 87–110

    Article  MATH  Google Scholar 

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

    Google Scholar 

  19. Fazel-Zarandi M, Fox S. Semantic matchmaking for job recruitment: an ontology-based hybrid approach. In: Proceedings of the 3rd International Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web at the 8th International Semantic Web Conference. 2010

    Google Scholar 

  20. Chen P C. A fuzzy multiple criteria decision making model in employee recruitment. IJCSNS International Journal of Computer Science and Network Security, 2009, 9(7): 113–117

    Google Scholar 

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

    Article  Google Scholar 

  22. Rodriguez M, Posse C, Zhang E. Multiple objective optimization in recommender systems. In: Proceedings of the 6th ACM Conference on Recommender systems. 2012, 11–16

    Chapter  Google Scholar 

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

    Google Scholar 

  24. DeCastro LN, Timmis J. Artificial immune systems: a novel paradigm for pattern recognition. Artificial Neural Networks in Pattern Recognition, 2002, 1: 67–84

    Google Scholar 

  25. Goldsby R A, Kindt T J, Osborne B. A. Kuby Immunology. 5th ed. New York: W. H. Freeman, 2002

    Google Scholar 

  26. De Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle. IEEE Transaction on Evolutionary Computation, 2002, 6(3): 239–251

    Article  Google Scholar 

  27. Aickelin U. Artificial immune systems (AIS): a new paradigm for heuristic decision making. In: Proceedings of the Annual Operational Research Conference. 2004

    Google Scholar 

  28. Su X, Khoshgoftaar T M. A Survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 421–425

    Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 1998, 43–52

    Google Scholar 

  31. 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 

  32. Drosou M, Pitoura E. Search result diversification. ACM SIGMOD Record, 2010, 39(1): 41–47

    Article  Google Scholar 

  33. Burke R. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 2002, 12(4): 331–370

    Article  MATH  Google Scholar 

  34. Herlocker J L, Konstan J A, Terveeni L G, Riedl J T. Evaluating collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): 5–53

    Article  Google Scholar 

  35. Sarwar B, Karypis G, Konstan J, Riedl J. Incremental SVD-based algorithms for highly scaleable recommender systems. In: Proceedings of the 5th International Conference on Computer and Information Technology. 2002

    Google Scholar 

  36. Schröder G, Thiele M, Lehner W. Setting goals and choosing metrics for recommender system evaluations. In: Proceedings of UCERSTI2 Workshop at the 5th ACM Conference on Recommender Systems. 2011

    Google Scholar 

  37. Tsuge S, Shishibori M, Kuroiwa S, Kita K. Dimensionality reduction using non-negative matrix factorization for information retrieval. In: Proceedings on IEEE International Conference on Systems, Man, and Cybernetics. 2001, 960–965

    Google Scholar 

  38. Hoyer P O. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 2004, 5: 1457–1469

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by Deanship of Scientific Research and Research Center of College of Computer and Information Sciences, King Saud University, Saudi Arabia.

Author information

Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

    Shaha Al-Otaibi

  2. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Shaha Al-Otaibi & Mourad Ykhlef

Authors
  1. Shaha Al-Otaibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mourad Ykhlef
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaha Al-Otaibi.

Additional information

Shaha Al-Otaibi is a lecturer in the Department of Information Systems, College of Computer and Information Systems, Nourah Bint Abdulrahman University, Saudi Arabia. She received her MS in computer science, and PhD in information system from King Saud University, Saudi Arabia. Her main research interests include data mining, E-learning and information security.

Mourad Ykhlef is an associate professor in the Department of Information Systems, College of Computer and Information Systems, King Saud University, Saudi Arabia. He got his MS in artificial intelligence and PhD in computer science from Paris 13 and Bordeaux 1, France, respectively. His main research interests include data mining and bio-inspired computing.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Otaibi, S., Ykhlef, M. Hybrid immunizing solution for job recommender system. Front. Comput. Sci. 11, 511–527 (2017). https://doi.org/10.1007/s11704-016-5241-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-016-5241-z

Keywords

Search

Navigation