Abstract
In this paper, we propose a Content-Based Recommendation Algorithm that takes into consideration the organization needs and the skills of candidate employees in order to quantify the suitability of a candidate employee for a specific job position. The proposed algorithm extends the Minkowski distance to perform a primary study in order to investigate how the Job Recruiting field could benefit further. Also we conduct an experimental evaluation with the objective of checking the quality and the effectiveness of the proposed algorithm. Our primary study produces promising results and shows that this algorithm can play an important role in the area of Job Recruiting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. In: Published by the IEEE Computer Society, IEEE Internet Computing, vol. 7(1), pp. 76–80 (2003)
Resnick, P., Varian, H.: Recommender systems. Commun. ACM 30(3), 56–58 (1997)
Burke, R., Felfernig, A., Goker, M.H.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2011)
Ramezani, M., Bergman, L., Thompson, R., Burke, R., Mobasher, B.: Selecting and applying recommendation technology. In: Proceeding of International Workshop on Recommendation and Collaboration, in Conjunction with 2008 International ACM Conference on Intelligent User Interfaces, Canaria, Canary Islands, Spain (2008)
Jobvite.: Social Recruitment Survey. http://recruiting.jobvite.com (2014).
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, Toronto, Canada (2010)
Drigas, A., Kouremenos, S., Vrettos, S., Vrettatos, J., Kouremenos, D.: An expert system for job matching of the unemployed. Expert Syst. Appl. 26, 217–224 (2004)
Fazel-Zarandi, M., Fox, M.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, Washington D.C., USA (2010)
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, IISA 2014, pp. 45–49. Chania, Greece (2014)
Minkowski, H.: Allgemeine Lehrsatze uber die konvexe Polyeder, Nachr. Ges. Wiss., Gottingen, 1897, 198–219 (=Ges. Abh., vol 2, pp. 103-121, Leipzig-Berlin, 1911)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Almalis, N., Tsihrintzis, G., Karagiannis, N. (2015). A New Content-Based Recommendation Algorithm for Job Recruiting. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-25032-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25030-4
Online ISBN: 978-3-319-25032-8
eBook Packages: Computer ScienceComputer Science (R0)