Abstract
In the Internet era, the enterprises and companies receive thousands of resumes from the job seekers. Currently available filtering techniques and search services help the recruiters to filter thousands of resumes to few hundred potential ones. Since these filtered resumes are similar to each other, it is difficult to identify the potential resumes by examining each resume. We are investigating the issues related to the development of approaches to improve the performance of resume selection process. We have extended the notion of special features and proposed an approach to identify resumes with special skill information. In the literature, the notion of special features have been applied to improve the process of product selection in E-commerce environment. However, extending the notion of special features for the development of approach to process resumes is a complex task as resumes contain unformatted text or semi-formatted text. In this paper, we have proposed an approach by considering only skills related information of the resumes. The experimental results on the real world data-set of resumes show that the proposed approach has the potential to improve the process of resume selection.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Times business solutions limited, July 24 (2000) http://www.timesjobs.com/
Flagship brand of monster worldwide, inc., July 27 (2009) http://www.monsterindia.com/
Info edge (india) ltd., July 30 (2008) http://www.naukri.com/
Agyemang, M., Barker, K., Alhajj, R.S.: Mining web content outliers using structure oriented weighting techniques and n-grams. In: Preneel, B., Tavares, S. (eds.) SAC 2005. LNCS, vol. 3897, pp. 482–487. Springer, Heidelberg (2006)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)
Ciravegna, F., Lavelli, A.: Learningpinocchio: adaptive information extraction for real world applications. Nat. Lang. Eng. 10(2), 145–165 (2004)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB 1998: Proceedings of the 24rd International Conference on Very Large Data Bases, pp. 392–403. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Liu, B., Ma, Y., Yu, P.S.: Discovering unexpected information from your competitors’ web sites. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 144–153. ACM, New York (2001)
Maheshwari, S., Reddy, P.: Discovering special product features for improving the process of product selection in e-commerce environment. In: ICEC 2009: Proceedings of the 11th international conference on Electronic commerce, Taipei, Taiwan. ACM, New York (2009)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. SIGMOD Rec. 29(2), 427–438 (2000)
Yu, K., Guan, G., Zhou, M.: Resume information extraction with cascaded hybrid model. In: ACL 2005: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Morristown, NJ, USA, pp. 499–506. Association for Computational Linguistics (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maheshwari, S., Sainani, A., Reddy, P.K. (2010). An Approach to Extract Special Skills to Improve the Performance of Resume Selection. In: Kikuchi, S., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2010. Lecture Notes in Computer Science, vol 5999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12038-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-12038-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12037-4
Online ISBN: 978-3-642-12038-1
eBook Packages: Computer ScienceComputer Science (R0)