Usage Context-Boosted Filtering for Recommender Systems in TEL

  • Katja Niemann
  • Martin Wolpers
Conference paper

DOI: 10.1007/978-3-642-40814-4_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8095)
Cite this paper as:
Niemann K., Wolpers M. (2013) Usage Context-Boosted Filtering for Recommender Systems in TEL. In: Hernández-Leo D., Ley T., Klamma R., Harrer A. (eds) Scaling up Learning for Sustained Impact. EC-TEL 2013. Lecture Notes in Computer Science, vol 8095. Springer, Berlin, Heidelberg

Abstract

In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach does not rely on the content of the learning objects or on the relations between users and learning objects but on the usage-based relations between the objects themselves. We take this new semantic similarity measure to enhance existing recommendation approaches for use in technology enhanced learning.

Keywords

item similarity recommender systems usage context 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Katja Niemann
    • 1
  • Martin Wolpers
    • 1
  1. 1.Schloss BirlinghovenFraunhofer Institute for Applied Information Technology FITSankt AugustinGermany

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