Introduction to Information Retrieval
Introduction to Information Retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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
GroupMe! - where semantic web meets web 2.0
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Folksonomies. Indexing and Retrieval in Web 2.0
Folksonomies. Indexing and Retrieval in Web 2.0
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
CROKODIL: a platform for collaborative resource-based learning
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Automatic discovery of complementary learning resources
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Recommender systems in e-learning have different goals as compared to those in other domains. This brings about new requirements such as the need for techniques that recommend learning resources beyond their similarity. It is therefore an ongoing challenge to develop recommender systems considering the particularities of e-learning scenarios like CROKODIL. CROKODIL is a platform supporting the collaborative acquisition and management of learning resources. It supports collaborative semantic tagging thereby forming a folksonomy. Research shows that additional semantic information in extended folksonomies can be used to enhance graph-based recommendations. In this paper, CROKODIL's folksonomy is analysed, focusing on its hierarchical activity structure. Activities help learners structure their tasks and learning goals. AScore and AInheritScore are proposed approaches for recommending learning resources by exploiting the additional semantic information gained from activity structures. Results show that this additional semantic information is beneficial for recommending learning resources in an application scenario like CROKODIL.