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.
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Tang, T., McCalla, G. (2004). Utilizing Artificial Learners to Help Overcome the Cold-Start Problem in a Pedagogically-Oriented Paper Recommendation System. In: De Bra, P.M.E., Nejdl, W. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2004. Lecture Notes in Computer Science, vol 3137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27780-4_28
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DOI: https://doi.org/10.1007/978-3-540-27780-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22895-0
Online ISBN: 978-3-540-27780-4
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