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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.

Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Jahr 2016
Sprache Englisch
Identifikator ISBN: 978-1-61208-471-8
URN: 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, Red Hook NY
Seiten 49-54
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