A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Understanding, building and using ontologies
International Journal of Human-Computer Studies
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Personalizing access to learning networks
ACM Transactions on Internet Technology (TOIT)
Learning services provisioning using semantic web technologies
Proceedings of the 1st International Conference on Intelligent Semantic Web-Services and Applications
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With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. To solve the problems of sharing and reusing teaching materials in different e-learning systems, presently several standard formats, including SCORM, IMS, LOM, and AICC, etc., have been proposed by several different international organizations. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of learning objects in a learning object repository by extended sharing and searching features. However, LOM is deficient in semantic-awareness operations in spite of its multifarious fields in describing a Learning Object. It is difficult for a learner, even for advanced learners, to completely specify so many terms when they are searching. This paper proposes a service-based framework for personalized learning objects retrieval and recommendation. The work of personalization harnesses the power of probabilistic semantic inference for query keywords, LOM-based user preference logging, and other users’ feedback for recommendation weighting to retrieve the most suitable learning object for users. An ontology-based query expansion algorithm and an integrated learning objects recommendation algorithm are also proposed.