Personalized Web Search Using Information Scent

Web Search engines explore the existing structure of the web and try to find documents that match the user search criteria .The major challenge for the search engines these days is to determine and satisfy the information need of the user searching the web efficiently and effectively. The approach presented in this paper is to personalize the web search according to the information need of the user using Information Scent in Query log mining. Query log is preprocessed to find the query sessions. Information need associated with query sessions is modeled using Information scent and content of clicked documents. The work done in this paper uses clustering techniques to cluster query sessions with similar information need modeled using Information Scent and content of clicked URLs. The information need of the current user session is used to identify the cluster that has the information need similar to the information need of the current session of the user. The selected cluster is used to recommend useful link on top of next requested result page. Recommended URLs will help the user to find the relevant documents which are closed to his needs and direct the search in a fruitful direction. This approach personalizes the search process to the need of user. Performance of the proposed approach is evaluated with an experimental study of query sessions mining of the “Google” search engine web history data and the experimental results shows the improvement of the Information Retrieval precision.