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Interest-based Recommendation in Academic Networks using Social Network Analysis

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

Abstract (englisch):
Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper, we investigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular value Decomposition (SVD)-based RS, Trust-based CF, and SNA-based techniques for recommending new collaborators and research topics to the researchers. The evaluation results show that SNA-based recommendation outperforms traditional CF methods in terms of coverage and thus can provide an effective solution to the sparsity and cold start problems in recommender systems.

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Volltext §
DOI: 10.5445/IR/1000059765
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2016
Sprache Englisch
Identifikator ISBN: 978-3-88579-656-5
KITopen-ID: 1000059765
Erschienen in GI Edition Proceedings Band 262 DeLFI 2016 – Die 14. E-Learning Fachtagung Informatik : 11.-14. September 2016 Potsdam. [Hrsg.] Ulrike Lucke
Verlag Köllen
Seiten 23-35
Schlagwörter Recommender Systems, Social Network Analysis, Academic Networks, Collaborative Filtering, Professional Learning
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