Web Page Recommendation Based on Semantic Web Usage Mining

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.