Trust in Personalized Web Search based on Clustered Query Sessions

Abstract

Personalized Web Search customizes the search according to the information need of the user on the Web. In Personalized Web search, search is personalized using the search results of those users profile which have the information need similar to that of present/current user. It is realized that similarity measure of the users profile is not the sufficient criterion for identifying the trusted users profile who are good in generating the effective personalized search results. One of the Research done in Personalized Web Search in [10] in which clustered user query sessions are used for generating the personalized web search results for the current user using the similarity measure alone. However the method proved to be effective but the use of similarity measure alone could not help in determining how good the clustered user profiles are in generating the reliable recommendations for effective personalized web search. In this paper an algorithm is proposed in which trust is introduced in personalized web search based on clustered query session for which trust metric is defined for clustered user sessions which measure the goodness of the clusters of query sessions in making the reliable recommendations for effective personalized web search and furthermore trust is not static but updated dynamically depending on the response of the user to the personalized search results generated by the selected trusted cluster. The proposed algorithm uses both the trust metric and the similarity measure for selecting the trusted cluster which is similar to the information need of the current user for the personalization of the web search effectively. Experiment was conducted on the clustered user query session to test the effectiveness of the proposed trust based personalized web search and results confirms the effectiveness of the proposed approach.