Artificial intelligence

Abstract: The Carrier Centre is information, analytical and organizational support of job placements of students and graduates. The information system for supporting all main activities was developed. Nowadays the system strengthens links between students and companies as repository of the CVs and vacancies. On the other side the system should be as a virtual recruiter that take into account student’s personal abilities and preferences, available jobs, Company profiles, local labour market infrastructure, industrial and technological trends, account job specification, available human resource to provide the effective decisions on employment. This paper presents the intelligent management system based on text mining methods for supporting recruitment services.

Keywords:

References:

1. Liu B., Lee W. S., Yu P., and Li X. 2002. Partially supervised classification of text documents. ICML-02. –Salton, G. and McGill, M. Introduction to Modern Information Retrieval. McGraw-Hill. – 1983.
2. Yang Y. and Pedersen J. P. A comparative study on feature selection in text categorization. ICML-97. – 1997.
3. Andrew McCallum, Rosenfeld R., Mitchell T, Ng A. Improving text clasification by shrinkage in a hierarchy ofclasses// In Proceedings of the International Conference on Machine Learning (ICML) – 1998. – P. 359-367.
4. Toutanova K., Chen F., Popat K., and Hofmann Th. Text classification in a hierarchical mixture model for smalltraining sets.// In Proceedings of the Tenth International ACM Conference on Information and KnowledgeManagement (CIKM). – 2001.
5. Joachims T. A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization,// In Proc. Of theICML’97. – 1997. – P. 143-151.
6. Zhao Y. and Karypis G. Evaluation of hierarchical clustering algorithms for document datasets // In Proceedingsof the International Conference on Information and Knowledge Management. – 2002.
7. Zhao Y. and Karypis G. Empirical and theoretical comparisons of selected criterion functions for documentclustering // Machine Learning. – 2004. – 55(3).
8. Shatovska T., Safonova T., Tarasov I. A Modified Multilevel Approach to the Dynamic Hierarchical Clusteringfor Complex types of Shapes. Lecture Notes in Informatics (LNI) // Proceeding. – 2007. – Vol. P-107 –P. 176-186.
9. Shatovska T., Safonova T., Tarasov I. The New Software Package for Dynamic Hierarchical Clustering forCircles Types of Shapes // Proceedings of XIII-th International Conference KDS. – 2007. – Varna (Bulgaria). –P. 125-129.
10. Karypis G., Han E.H., Kumar V. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling,IEEE Computer: Special Issue on Data Analysis and Mining. – 1999. – Vol. 32(8). – P. 68-75.
11. Karypis G. and Kumar V. Multilevel k-way hypergraph partitioning // Proceedings of the Design and AutomationConference. – 1999.
12. Russian stemming algorithm, 2005 [Электронный ресурс]. – Режим доступа:http://snowball.tartarus.org/algorithms/russian/stemmer.html.
13. Keleberda I., Repka V., Biletskiy Y. Building learner's ontologies to assist personalized search of learning objects.ICEC 2006. – P. 569-573.