Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
KAON - Towards a Large Scale Semantic Web
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web usage mining: discovery and application of interesting patterns from web data
Web usage mining: discovery and application of interesting patterns from web data
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
IEEE Transactions on Knowledge and Data Engineering
Semantic similarity methods in wordNet and their application to information retrieval on the web
Proceedings of the 7th annual ACM international workshop on Web information and data management
Perspectives on ontology-based querying: Research Articles
International Journal of Intelligent Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
Content-based recommendation systems
The adaptive web
A comparative study of ontology based term similarity measures on PubMed document clustering
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Web page recommendation based on semantic web usage mining
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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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.