Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2009)
Sept. 15, 2009 to Sept. 18, 2009
This paper introduces a multi-model ontology-based framework for semantic search of educational content in E-learning repository of courses, lectures, multimedia resources, etc. This hybrid recommender system is driven by two types of recommendations: content-based (domain ontology model) and rule-based (learner’s interest-based and cluster-based). The domain ontology is used to represent the learning materials. In this context, the ontology is composed by a hierarchy of concepts and sub-concepts. Whereas, the learner’s ontology model represents a subset of the domain ontology, and the cluster-based recommendations are added as additional semantic recommendations to the model. Combining the content-based with the rule-based provides the user with hybrid recommendations. All of them influenced the re-ranking of the retrieved documents with different weights. Our proposed approach has been implemented on the HyperManyMedia1 platform.
Recommender System, Personalization, Information Retrieval, Ontology, Semantic
Elizabeth Romero, Robert Wyatt, Leyla Zhuhadar, Olfa Nasraoui, "Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain", Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 03, no. , pp. 91-95, 2009, doi:10.1109/WI-IAT.2009.238