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SNA-Based Recommendation in Professional Learning Environments

Chatti, Mohamed Amine; Toreini, Peyman; Thues, Hendrik; Schroeder, Ulrik

Abstract:
Recommender systems can provide effective means to support self-organization and networking in professional learning environments. In this paper, we leverage social network analysis (SNA) methods to improve interest-based recommendation in professional learning networks. We discuss two approaches for interest-based recommendation using SNA and compare them with conventional collaborative filtering (CF)-based recommendation methods. The user evaluation results based on the ResQue framework confirm that SNA-based CF recommendation outperform traditional CF methods in terms of coverage and thus can provide an effective solution to the sparsity and cold start problems in recommender systems.


Volltext §
DOI: 10.5445/IR/1000056942
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2016
Sprache Englisch
Identifikator ISBN: 978-1-61208-471-8
urn:nbn:de:swb:90-569422
KITopen-ID: 1000056942
Erschienen in eLmL 2016 : The Eighth International Conference on Mobile, Hybrid, and On-line Learning, 24. - 28. Apr 2016, Venice, Italy
Verlag Curran Associates, Inc.
Seiten 49-54
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