Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
PeRES: a personalized recommendation education system based on multi-agents & SCORM
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
An evolving recommender-based framework for virtual learning communities
International Journal of Web Based Communities
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We develop an e-learning web application that integrates the materials recommender system to facilitate the learners during the learning process. The system evaluates each learner via the quiz generator by randomly selecting a set of questions that are created by the instructor. Our smart e-learning system helps instructors to create and maintain both compulsory materials and questions. We implemented the system at the faculty of Resource and Environment, Kasetsart University at Sri-racha campus and found that our system got a very good response from the instructors and learners. Furthermore, we propose the global e-learning framework using web service that has an ability to aggregate the recommended materials from other e-learning web sites and predicts more suitable materials to learners.