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SNA-Based Recommendation in Professional Learning Environments

Authors:
Mohamed Amine Chatti
Peyman Toreini
Hendrik Thüs
Ulrik Schroeder

Keywords: Professional Learning; Recommender Systems; Collaborative Filtering, Social Network Analysis

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.

Pages: 49 to 54

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-471-8

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016