Contexts in a paper recommendation system with collaborative filtering
Pinata Winoto, Tiffany Tang, Konkuk University, Chungju City ; Gordon McCalla, University of Saskatchewan
IRRODL Volume 13, Number 5, ISSN 1492-3831 Publisher: Athabasca University Press
Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios.
Winoto, P., Tang, T. & McCalla, G. (2012). Contexts in a paper recommendation system with collaborative filtering. The International Review of Research in Open and Distributed Learning, 13(5), 56-75. Athabasca University Press.
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A Computational Method for Enabling Teaching-Learning Process in Huge Online Courses and Communities
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The International Review of Research in Open and Distributed Learning Vol. 18, No. 1 (Feb 28, 2017)
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