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A Multidimensional Paper Recommender: Experiments and Evaluations
July/August 2009 (vol. 13 no. 4)
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.

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Index Terms:
Paper recommender systems, e-learning, information filtering, Internet
Citation:
Tiffany Y. Tang, Gordan McCalla, "A Multidimensional Paper Recommender: Experiments and Evaluations," IEEE Internet Computing, vol. 13, no. 4, pp. 34-41, July-Aug. 2009, doi:10.1109/MIC.2009.73
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