A Multidimensional Paper Recommender: Experiments and Evaluations

Authors:
Tiffany Y. Tang;Gordan McCalla
Affiliations:
Konkuk University;University of Saskatchewan
Venue:
IEEE Internet Computing
Year:
2009

Citing 0
Cited 3

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Educational resources recommendation system based on agents and semantic web for helping students in a virtual learning environment

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Abstract

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.