Fab: content-based, collaborative recommendation
Communications of the ACM
Online Communities: Designing Usability and Supporting Socialbilty
Online Communities: Designing Usability and Supporting Socialbilty
Cultivating Communities of Practice: A Guide to Managing Knowledge
Cultivating Communities of Practice: A Guide to Managing Knowledge
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Using technology to transform communities of practice into knowledge-building communities
ACM SIGGROUP Bulletin - Special issue on online learning communities
IEEE Transactions on Knowledge and Data Engineering
A multilayer ontology-based hybrid recommendation model
AI Communications - Recommender Systems
Towards an Ontology for Supporting Communities of Practice of E-Learning "CoPEs": A Conceptual Model
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
ReMashed --- Recommendations for Mash-Up Personal Learning Environments
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Collaborative filtering recommender systems
The adaptive web
Evaluating collaborative filtering recommendations inside large learning object repositories
Information Processing and Management: an International Journal
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges
IEEE Transactions on Learning Technologies
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The paper discusses the application of the Information Filtering (IF) approach in Communities of Practice of E-learning (CoPEs). We identify the main characteristics of CoPEs and show how the integration of the IF techniques can be useful in this context as a technology support for members of CoPEs. A personalized recommendation approach is proposed for CoPEs based on the hybrid semantic IF, integrating the content-based filtering, the collaborative filtering and the ontology-based filtering approaches. Three strategies of recommendation have been proposed: (1) a semantic recommendation by specialty; (2) a semantic content-based recommendation by domains of interests; and (3) a semantic collaborative recommendation by domains of interests. We have developed a prototype of a recommendation system called ReCoPESyst, based on the recommendation approach. In order to evaluate our system, we considered a community of teachers from a higher education context. A preliminary tests and experimentation of ReCoPESyst conducted within this community show its advantage and benefit for members.