Abstract
Lots of researches show that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Meanwhile Web Usage Mining plays an important role in recommender systems and web personalization. However, not many studies have been focused on how to combine the two methods for recommender systems. In this paper, we propose a hybrid recommender system based on ontology and Web Usage Mining. The first step of the approach is extracting features from web documents and constructing relevant concepts. Then build ontology for the web site use the concepts and significant terms extracted from documents. According to the semantic similarity of web documents to cluster them into different semantic themes, the different themes imply different preferences. The hybrid approach integrates semantic knowledge into Web Usage Mining and personalization processes. The experimental results show that the combination of the two approaches can improve the precision rate, coverage rate and matching rate effectively.
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Wei, L., Lei, S. (2009). Integrated Recommender Systems Based on Ontology and Usage Mining. In: Liu, J., Wu, J., Yao, Y., Nishida, T. (eds) Active Media Technology. AMT 2009. Lecture Notes in Computer Science, vol 5820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04875-3_16
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DOI: https://doi.org/10.1007/978-3-642-04875-3_16
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