GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Human-Computer Interaction
E-learning Recommendation System
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
Review: Ambient intelligence: Technologies, applications, and opportunities
Pervasive and Mobile Computing
Providing Personalized Courses in a Web-Supported Learning Environment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Location-based service with context data for a restaurant recommendation
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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Taking into account continuously growing content wealth of pervasive environments generally, user needs assistance to find what he want in short time. Specifically in pervasive learning environment where learners are surrounded by numerous suppliers and the rich resources offered by the learning platform, the personalized recommender systems seems important for providing user by convenience and fulfil his needs. However, existing learning recommender systems interest to the user appraisal and taste while the contextual constraints due to the heterogeneity may influence the service consumption. We propose in this paper an hybrid recommender approach based on contextual information and memory-based filtering.