The last decade, there has been tremendous growth in the field of network. The information served to the internet users through web is enormous. Some information provided is of use to the end users, and others of no use to them. Current web information gathering systems attempt to satisfy user requirements by capturing their information needs. For this purpose, user profiles are created for user background knowledge description. The users’ interests in user profiles, a personalized search middleware is able to adapt the search results obtained from general search engines to the users’ preferences through personalized reranking of the search results. The conceptual relationship between the documents has to be represented in order to identify the information that a user wants from those represented concepts. To represent the semantic relation, the ontology is used here. To build a user profile , the Web pages that the user visited are monitored and the system represents the long-term and short-term preference weights as the preference ontology after inferring relevant concepts from the general ontology. a user submits a query, the search results are obtained from the backend search engines (e.g. Google, MSNSearch, and Yahoo). The search results are combined and reranked according to the user’s profile trained from the user’s previous search activities. the search results are obtained from the backend search engines, the content and location concepts (i.e. important terms and phrases) and their relationships are mined online from the search results and stored, respectively, as content ontology and location ontology. The content and location ontologies, along with the clickthrough data, are then employed in RSVM training to obtain a content weight vector and a location weight vector for reranking the search results for the user.
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