Ing. Stanislav Kuznetsov

Publikace

Reducing Cold Start Problems in Educational Recommender Systems

Rok
2016
Publikováno
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.
Typ
Stať ve sborníku
Anotace
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

Rok
2015
Publikováno
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.
Typ
Stať ve sborníku
Anotace
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

Rok
2014
Publikováno
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
Typ
Stať ve sborníku
Anotace
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.