GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Comprehensive personalized information access in an educational digital library
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Paper Annotation with Learner Models
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
| Hi-index | 0.00 |
In our previous work [9, 10], we highlighted the importance of incorporating learners' pedagogical features in making paper recommendations and proposed the pedagogical-oriented paper recommender. In this paper, we report our studies in designing and evaluating a six-dimensional paper recommender. Experimental results from a human subject study of learner preferences suggest that in the e-learning domain where there are not enough papers as well as learners to start up the recommender system (RS), it is imperative to inject other factors such as the overall popularity of each paper, learner knowledge background, etc., although these factors are less important for making recommendations on movies, books, CDs.