Current web-search engines provide query results with relatively high precision and recall, but user satisfaction is often low. Precision and recall are two traditional measurement methods of data accuracy but not user satisfaction or preferences. Therefore, the user characteristics and preference have become important in development of information retrieval approaches. Although many recommendation systems are designed to provide personalized query results to match user preferences in order to increase user satisfaction, none of these systems was designed to include semantics flexibly and efficiently to provide constant-timed online recommendation.; The goal of this research is to test the hypothesis that a semantics-based system incorporating semantic data representation structures (ontologies) can facilitate customization of online search results. We developed a hybrid recommendation system which is implemented with a semantic-based similarity decision model. This semantic-based model utilizes Directed Acyclic Graph (DAG) structures and support constant-time online recommendation for the recommendation system design. The recommendation system is designed to solve scalability and sparsity problems, and generate constant-timed online recommendation. The semantic-based model is compatible with current Semantic Web technologies. Experiments and a system have been designed to examine the effects of customized information retrieval.
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