Automatic recommender systems for education providers

Type Research Project

Duration Jan. 1, 2001 - Dec. 31, 2003

  • Institute for Data, Process and Knowledge Management (AE Mendling) AE (Details)
  • Institute for Data, Process and Knowledge Management (AE Polleres) AE (Details)

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  • Hahsler, Michael (Former researcher) Project Head
 

Abstract (German)

Es wird die Rolle von Vorschlagssystemen für Bildung und Forschung im Rahmen einer Virtuellen Universität untersucht. Insbesondere werden Techniken zum automatischen Tutoring für Massenuniversitäten untersucht.

Abstract (English)

We investigate the role of recommender systems and their potential in the educational and scientific environment of a Virtual University.
The key idea is to use the information aggregation capabilities of a recommender system to improve the tutoring and consulting services of a Virtual University in an automated way and thus scale tutoring and consulting in a personalized way to a mass audience.

Partners

  • Universität Karlsruhe (TH), Lehrstuhl für Informationsdienste und elektronische Märkte - Germany http://www.em.uka.de/

Publications

Journal article

2002 Gaul, Wolfgang, Geyer-Schulz, Andreas, Hahsler, Michael, Schmidt-Thieme, Lars. 2002. eMarketing mittels Recommendersystemen. Marketing ZFP, 24, 47-55 (Details)
2001 Geyer-Schulz, Andreas, Hahsler, Michael, Jahn, Maximillian. 2001. Educational and scientific recommender systems: Designing the information channels of the virtual university. International Journal of Engineering Education, 17, 2, 153-163 (Details)

Chapter in edited volume

2003 Geyer-Schulz, Andreas, Hahsler, Michael. 2003. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, LNAI 2703 (Details)
2002 Geyer-Schulz, Andreas, Hahsler, Michael, Jahn, Maximillian. 2002. A customer purchase incidence model applied to recommender systems. In: Kohavi, R., Masand, B. M., Spiliopoulou, M., Srivastava, J. (eds.): WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, LNCS/LNAI 2356 (Details)

Contribution to conference proceedings

2005 Hahsler, Michael. 2005. ePubWU - Erfahrungen mit einer Volltext an der Wirtschaftsuniversität Wien. In Bibliotheken - Fundament der Bildung, 28. Österreichischer Bibliothekartag 2004, Hrsg. Christian Enichlmayr, 190-193. Linz: (Details)
2003 Geyer-Schulz, Andreas, Hahsler, Michael, Thede, Anke. 2003. Comparing association-rules and repeat-buying based recommender systems in a b2b environment. In: Schader, M., Gaul, W., Vichi, M. (eds.): Between Data Science and Applied Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, Springer (Details)
2002 Böhm, W., Geyer-Schulz, A., Hahsler, M., Jahn, M.. 2002. Repeat buying theory and its application for recommender services. In: Exploratory Data Analysis in Empirical Research. Proceedings of the 25th Annual Conference of the GfKl, University of Munich, March 14-16, 2001, Hrsg. Opitz, O., Schwaiger, M., 229-239. Springer-Verlag (Details)

Classification

  • 1109 Information and data processing (Details)

Expertise

  • Virtual University
  • Virtual University
  • Recommender Systems
  • Data Mining
  • Artificial Intelligence