An Efficient Hybrid User Profile Based Web Search Personalization Through Semantic Crawler

The World Wide Web has a collection of trillions of web pages and these web pages are increasing day by day. Web pages available on the Web change frequently and these changes are sometimes unnoticeable by the end user. The exponential growth of web documents on the Internet makes it difficult to find out which are the most relevant web documents for a particular end user on a given search query and user spends a lot of time to get relevant information. The proposed web search personalization system successfully identifies preferences of the end user and constructs an efficient user profile for every end user. It utilizes the built web page repository by retrieving web search results and personalizes them according to user preferences. It is also capable to handle ambiguous queries. The proposed system increases the search accuracy by 37.6% over existing agent based personalization system.