An Implicit-Feedback Based Ranking Methodology For Web Search Engines

The World Wide Web (WWW) is a fast growing network of information covering nearly every possible topic. With the input of a few keywords a search engine can return a list of relevant web pages by querying its index. However, it is quite common to witness irrelevant results being presented to the user. One way to improve the ordering of the search results is by incorporating user feedback in ranking the documents for relevancy. We present a model for search engine enhancement by using implicit feedback in the form of ClickThrough data from the users. The order of the links returned as the query result is re-arranged for the future queries based on the choices made by the majority of the users. An algorithm, with its implementation, is presented and then evaluated to demonstrate its capability as an add-on component for enhancement of the current ranking algorithms