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Due to the tremendous increase in use of internet surfing user may not get the most accurate search result which they preferred for their queries to clarify their known uncertain information. Such search engine may not often bring the user preferred information and does not satisfy the request completely. Hence it is necessary to infer and get user specific interest about a topic. Providing results just based on user's previous search history does not yield needful results since the self feedback and repeatable feedback were not included in the existing system. Those types of problems are solved in proposed system. So this proposal aims at the vigorous user feedback to provide accurate search specific results and to increase the performance of the search engine. This feedback is captured for all relevant URL that matches the search query using Classified Average Precision algorithm to yield most accurate web search results. Results from the clustered search are then categorized with captured feedback there by calculating the distance between the no of clicked links and user requested information in order to provide the exact information to the user. Likewise we can also search the image data based on the input which we giving as related image of our search result. Both images will be compared by calculating the histogramic values of images and the graph related to those values should be generated.

Classified Ave Rage Precision For Capturing User Search Intention For Multiattributes