Fab: content-based, collaborative recommendation
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Artificial Life
Proceedings of the Second International Conference on Intelligent Tutoring Systems
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
International Journal of Learning Technology
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Adaptive neuro-fuzzy pedagogical recommender
Expert Systems with Applications: An International Journal
Intelligent Decision Technologies - Special issue on Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments
Information Processing and Management: an International Journal
A contextual semantic representation of learning assets in online communities of practice
International Journal of Metadata, Semantics and Ontologies
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Lifelong learners who select learning activities to attain certain learning goals need to know which are suitable and in which sequence they should be performed. Learners need support in this way-finding process, and we argue that this could be provided by using Personalised Recommender Systems (PRSs). To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way-finding presents personalised recommendations in relation to information about learning goals, learning activities and learners. A PRS has been developed according to this model, and recommends to learners the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.