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Issue No. 04 - July/August (2009 vol. 13)
ISSN: 1089-7801
pp: 34-41
Tiffany Y. Tang , Konkuk University
Gordan McCalla , University of Saskatchewan
Paper recommender systems in the e-learning domain must consider pedagogical factors, such as a paper's overall popularity and learner background knowledge — factors that are less important in commercial book or movie recommender systems. This article reports evaluations of a 6D paper recommender. Experimental results from a human subject study of learner preferences suggest that pedagogical factors help to overcome a serious cold-start problem (not having enough papers or learners to start the recommender system) and help the system more appropriately support users as they learn.
Paper recommender systems, e-learning, information filtering, Internet

T. Y. Tang and G. McCalla, "A Multidimensional Paper Recommender: Experiments and Evaluations," in IEEE Internet Computing, vol. 13, no. , pp. 34-41, 2009.
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