Issue No. 04 - July/August (2009 vol. 13)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIC.2009.73
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.