Metadata domain-knowledge driven search engine in "HyperManyMedia" E-learning resources
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Metadata as seeds for building an ontology driven information retrieval system
International Journal of Hybrid Intelligent Systems
Contextual web searches in Facebook using learning materials and discussion messages
Computers in Human Behavior
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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We present an approach for personalized retrieval in an e-learning platform, that takes advantage of semantic Web standards to represent the learning content and the user/learner profiles as ontologies, and that re-ranks search results/lectures based on how the contained terms map to these ontologies. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner's context can be effectively used for improving the precision and recall in e-learning content retrieval, particularly by re-ranking the search results based on the learner's past activities.