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.
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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. https://doi.org/10.1007/978-3-642-40814-4_20
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DOI: https://doi.org/10.1007/978-3-642-40814-4_20
Publisher Name: Springer, Berlin, Heidelberg
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