Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70022
Title: Evaluating a smart recommender for an evolving e-learning system : a simulation-based study
Authors: Tang, TY
Mccalla, GI
Issue Date: 2004
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3060, p. 439-443
Abstract: In this paper we discuss paper recommendation techniques for learners in an evolvable e-learning system. We carried out an experiment using artificial learners for two techniques. The first one is based on the matching of learner model to the papers (pure model-based recommendation). And the second technique is based on peer learner recommendation (hybrid collaborative filtering), which is relatively domain independent. Experimental results show that hybrid collaborative filtering, which we believe can lower computational costs, will not compromise the overall performance of the recommender system.
Keywords: x
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 978-3-540-22004-6
978-3-540-24840-8
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-24840-8_34
Description: Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, London, Ontario, Canada, May 17-19, 2004
Appears in Collections:Conference Paper

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