Utilizing artificial learners to help overcome the cold-start problem in a pedagogically-oriented paper recommendation system

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. © Springer-Verlag 2004.