Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70084
Title: Utilizing artificial learners to help overcome the cold-start problem in a pedagogically-oriented paper recommendation system
Authors: Tang, TY
Mccalla, GI
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3137, p. 245-254 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In this paper we discuss the cold-start problem in an evolvable paper recommendation e-learning system. We carried out an experiment using artificial and human learners at the same time. Artificial learners are used to solve the cold-start recommendation problem when no paper has been rated by the learners. Experimental results are encouraging, showing that using artificial learners achieves better performance in terms of learner subjective ratings; and more importantly, human learners are satisfied with the recommendations received.
Description: Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2004, Eindhoven, The Netherlands, August 23-26, 2004
URI: http://hdl.handle.net/10397/70084
ISBN: 978-3-540-22895-0
978-3-540-27780-4
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-27780-4_28
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