Ing. Stanislav Kuznetsov, Ph.D.

Publications

Reducing Cold Start Problems in Educational Recommender Systems

Authors
Kuznetsov, S.; Kordík, P.; Řehořek, T.; Kroha, P.; Dvořák, J.
Year
2016
Published
2016 International Joint Conference on Neural Networks (IJCNN). San Francisco: American Institute of Physics and Magnetic Society of the IEEE, 2016. p. 3143-3149. ISSN 2161-4407. ISBN 978-1-5090-0620-5.
Type
Proceedings paper
DOI
10.1109/IJCNN.2016.7727600
Departments
Department of Theoretical Computer Science
Department of Software Engineering
Annotation
Educational data can help us to personalise university information systems. In this paper, we show how educational data can be used to improve the performance of interaction-based recommender systems. Educational data is transformed to student profiles helping to prevent cold start problems when recommending projects to students with few user interactions. Our results show that our hybrid interaction based recommender boosted by educational profiles significantly outperforms bestseller recommendation, which is a mainstream recommendation method for cold start users.

Mining skills from educational data for project recommendations

Authors
Kuznetsov, S.; Kordík, P.
Year
2015
Published
Proceedings of the International Joint Conference CISIS’15 and ICEUTE’15. Berlin: Springer-Verlag, 2015, pp. 617-627. Advances in Intelligent Systems and Computing. ISSN 2194-5357. ISBN 978-3-319-19713-5.
Type
Proceedings paper
DOI
10.1007/978-3-319-19713-5_54
Departments
Department of Theoretical Computer Science
Annotation
We are focusing on an issue regarding how to actually recognize the skills of students based on educational results. Existing approaches do not offer suitable solutions. This paper will introduce algorithms making possible to aggregate educational results using ontology. We map the aggregated results, using various methods, as skills that are understandable for external partners and usable to recommend students for projects and projects for students. We compare the results of individual algorithms with subjective assessments of students, and we apply a recommendation algorithm that closely models these skills.

Utilizing educational data in collaboration with industry

Authors
Kordík, P.; Kuznetsov, S.
Year
2014
Published
Proceedings of the 13th Annual Conference Znalosti 2014. Praha: VŠE, 2014, pp. 38-47. ISBN 978-80-245-2054-4. Available from: http://znalosti.eu/images/accepted_papers/znalosti2014_paper23.pdf
Type
Proceedings paper
Departments
Department of Theoretical Computer Science
Annotation
Universities are seldom using their data efficiently. In this case study, we show how educational data can be used to recommend suitable students for project, get feedback from industrial partners, help students to focus on skills that are demanded by companies. We have developed portal for students collaboration with industrial partners and run it in a pilot for almost a year. Based on our observations described in this contribution, we are adjusting the portal to enhance the functionality and streamline processes supported by the portal.