SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Toward Recommendation Based on Ontology-Powered Web-Usage Mining
IEEE Internet Computing
Using domain ontology for semantic web usage mining and next page prediction
Proceedings of the 18th ACM conference on Information and knowledge management
Integrated Recommender Systems Based on Ontology and Usage Mining
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Web Semantics: Science, Services and Agents on the World Wide Web
Using Ontology and Sequence Information for Extracting Behavior Patterns from Web Navigation Logs
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Improving pattern quality in web usage mining by using semantic information
Knowledge and Information Systems
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The growth of the web has created a big challenge for directing the user to the Web pages in their areas of interest. Meanwhile, web usage mining plays an important role in finding these areas of interest based on user's previous actions. The extracted patterns in web usage mining are useful in various applications such as recommendation. Classical web usage mining does not take semantic knowledge and content into pattern generations. Recent researches show that ontology, as background knowledge, can improve pattern's quality. This work aims to design a hybrid recommendation system based on integrating semantic information with Web usage mining and page clustering based on semantic similarity. Since the Web pages are seen as ontology individuals, frequent navigational patterns are in the form of ontology instances instead of Web page addresses, and page clustering is done using semantic similarity. The result is used for generating web page recommendations to users. The recommender engine presented in this paper which is based on semantic patterns and page clustering, creates a list of appropriate recommendations. The results of the implementation of this hybrid recommendation system indicate that integrating semantic information and page access sequence into the patterns yields more accurate recommendations.