A Personalized Job Recommended System Using Hybrid Collaborative Filtering Algorithm

Authors(4) :-N. Rajganesh, S. Seetha Devi, J. Keerthana, R. Poovizhi

Job recommendation systems usually involve exploiting the relations among known features and content that describe jobs. Implement the interface with personalization and profile based search for job recommendations. Construct the user profiles based on job type, interest, location and date. Combine content and collaborative filtering approach to recommend the jobs with improved accuracy rate. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as hybrid collaborative filtering.

Authors and Affiliations

N. Rajganesh
Assitant Professor, Department of Information Technology,A.V.C College of Engineering, Tamil Nadu, India
S. Seetha Devi
UG Student, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India
J. Keerthana
UG Student, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India
R. Poovizhi

Job Recommender, Information Retrieval,Semantic Matching,Person Similarities.

  1. S. T. Al-Otaibi and M. Ykhlef, "A survey of job recommender systems",International Journal of the Physical Sciences, vol. 7(29), pp. 5127-5142, July, 2012.
  2. S. T. Zheng, W. X. Hong, N. Zhang and F. Yang, "Job recommender systems: a survey,"In Proceedings of the 7th International Conference on Computer Science & Education (ICCSE 2012), pp. 920-924, Melbourne, Australia, July, 2012.
  3. M. Gao and Y. Q. Fu, "User-Weight Model for Item-based Recommendation Systems," Journal of Software, vol. 7(9), pp. 2133-2140, 2012.
  4. K. Yu, G. Guan and M. Zhou, "Resume information extraction with cascaded hybrid model," In Proceedings of the 43rd Annual Meeting of the ACL, pp. 499-506, Ann Arbor, Michigan, June, 2005.
  5. X. Yi, J. Allan and W. B. Croft, "Matching resumes and jobs based on relevance models,"In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and DevelopmentinInformationRetrieval,PP. 809-810, Amsterdam, The Netherlands,2007.
  6. I. Paparrizos, B. B. Cambazoglu and A. Gionis, "Machine learned job recommendation, " In Proceedings of the fifth ACM Conference on Recommender Systems, pp. 325-328, Chicago, USA, October, 2011.
  7. J. S. Breese, D. Heckerman and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, " In Proceedings of the 14thConference on Uncertainty in Artificial Intelligence, pp. 42-52, 1998.
  8. G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a surveyof the state- of-the-art and possible extensions,"Knowledge and Data Engineering, IEEE Transactions on, vol. 17(6), pp. 734- 749, 2005.
  9. H. W. Ye, "A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map,"Journal of Software, vol. 6(4), pp.732-739, 2011.
  10. L. Hu, W. B. Wang, F. Wang, X. L. Zhang and K. Zhao, "The Design and Implementation of Composite Collaborative Filtering Algorithm for Personalized Recommendation,"Journal of Software, vol. 7(9), pp. 2040-2045,2012.
  11. F. Farber , T. Weitzeland T. Keim, "An automated recommendation approach to selection in personnel recruitment,"In Proceedings of the 2003 Americas Conference on Information Systems, pp. 2329-2339, Tampa, USA, 2003.
  12. R. Burke, "Hybrid recommender systems: survey and experiments , " User Modeling and User-Adapted Interaction, vol. 12(4), pp. 331-370, 2002.
  13. M. Fazel-Zarandiand M. S. Fox, "Semantic matchmaking for job recruitment an ontolgy 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, Washington D. C., USA, 2010.
  14. R. Rafter, K. Bradley and B. Smyth, "Personalised retrieval for online recruitment services,"In Proceedings of the 22nd Annual Colloquium on IR Research, Cambridge, UK, 2010.

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 191-196
Manuscript Number : CSEIT183350
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

N. Rajganesh, S. Seetha Devi, J. Keerthana, R. Poovizhi, "A Personalized Job Recommended System Using Hybrid Collaborative Filtering Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.191-196, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT183350

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