Abstract
Research on e-learner community building has attracted much attention for its effectiveness in sharing the learning experience and resources among geographically dispersed e-learners. While collaborative filtering proves its success as one of the most efficient methods in finding similar users in e-commerce domain, it does meet special challenges in e-learning areas. In this paper, we incorporate multi-agent techniques into collaborative filtering and propose a novel community building scheme. By doing so, we manage to collect useful information from the learner behaviors and thus increase the scalability and flexibility of traditional collaborative filtering methods. The experiment on a standard benchmark shows that our scheme has reasonable community building quality and e-learners can make better recommendations to each other inside the community.
Supported by the National High-Tech Research and Development Plan of China under Grant No 2003AA4Z3020
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© 2005 Springer-Verlag Berlin Heidelberg
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Sun, Y., Han, P., Zhang, Q., Zhang, X. (2005). A Novel Multi-agent Community Building Scheme Based on Collaboration Filtering. In: Lau, R.W.H., Li, Q., Cheung, R., Liu, W. (eds) Advances in Web-Based Learning – ICWL 2005. ICWL 2005. Lecture Notes in Computer Science, vol 3583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11528043_21
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DOI: https://doi.org/10.1007/11528043_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27895-5
Online ISBN: 978-3-540-31716-6
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