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Dataset-driven research for improving recommender systems for learning

 
: Verbert, Katrien; Drachsler, Hendrik; Manouselis, Nikos; Wolpers, Martin; Vuorikari, Riina; Duval, Erik

:
Postprint urn:nbn:de:0011-n-1944746 (260 KByte PDF)
MD5 Fingerprint: b8480397a247fa019c72c7a630b17beb
© ACM 2011 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Erstellt am: 10.2.2012


Long, P. ; Association for Computing Machinery -ACM-:
1st International Conference on Learning Analytics and Knowledge, LAK 2011. Proceedings : Banff, AB, Canada, February 27 - March 01, 2011
New York: ACM, 2011
ISBN: 978-1-4503-0944-8
S.44-53
International Conference on Learning Analytics and Knowledge (LAK) <1, 2011, Banff>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()
information search and retrieval; computers and education

Abstract
In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or Each- Movie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for learning. We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms.

: http://publica.fraunhofer.de/dokumente/N-194474.html