Title :
A Multidimensional Paper Recommender: Experiments and Evaluations
Author :
Tang, Tiffany Y. ; McCalla, Gordon
Author_Institution :
Konkuk Univ., Seoul, South Korea
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.
Keywords :
Internet; computer aided instruction; information filters; e-learning domain; multidimensional paper recommender system; pedagogical factor; Books; Collaboration; Electronic learning; Filtering; Humans; Internet; Matched filters; Motion pictures; Multidimensional systems; Recommender systems; Internet; Paper recommender systems; e-learning; information filtering;
Journal_Title :
Internet Computing, IEEE
DOI :
10.1109/MIC.2009.73
