Hybrid Machine Learning Procedure for Pattern Evaluation and Dynamic Query Formation in Personalized Web Search
Searching is one of the common tasks performed on the Internet. The information on the web is growing dramatically. The users have to spend lots of time on the web finding the information they are interested in. Existing method has demonstrated the privacy for the personalized web search by using UPS (user customizable privacy preserving search). The utility of the data is limited to statistical information and it is not clear how it can be used for personalized web search. For retrieving the user query results, it takes high computational and communication time and also cost. In this paper to better support users in their long-term information quests on the Web, search engines keep track of their queries and clicks while searching online. We propose CPHC algorithm for pattern Evaluation and dynamic query formation in PWS of each user present in the data base application procedure. We provide secure privacy to search profiles of each users using hashing secure algorithms. It show efficient security operations of each user based on processing of personalized web search and also find the dynamic query formation by using pattern evaluation. Through this we can hide the user search results. So that relevant information will be provided to the user with in a less computational time and communicational time, and it achieves better accuracy when compared with the Existing Works.
Keywords - Personalized web search, Hierarchical Clustering, Pattern Evaluation, Dynamic Query Formation.