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
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
eLearning at Rhodes University " A Case Study
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The Design of Web-Based Personal Collaborative Learning System (WBPCLS) for Computer Science Courses
ICWL '08 Proceedings of the 7th international conference on Advances in Web Based Learning
Groupized learning path discovery based on member profile
ICWL'10 Proceedings of the 2010 international conference on New horizons in web-based learning
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E-learning systems , as an education pattern, are becoming more and more popular. In e-learning systems, courseware management is an indispensable part. As the number of various courseware increases, how to find the courseware or learning materials that are most suitable to users and users of e-learning systems are most interested in is a practical problem. In this paper, we apply the idea of knowledge discovery techniques to make personalized recommendation for courseware. We design the courseware recommendation algorithm which combines contents filtering and collaborative filtering techniques. Also we propose the architecture of courseware management system with courseware recommendation, which is seamlessly integrated in our E-learning system. The experiment shows that our algorithm is able to truly reflect users’ interests with high efficiency.