Abstract

ABSTRACT A web search engine is a tool designed to search for information on the World Wide Web. The search results are usually presented in a list. The information may consist of web pages, images, information and other types of files. Some search engines also mine data available in databases. Unlike Web directories, which are maintained by human editors, search engines operate algorithmically or are a mixture of algorithmic and human input. The Search Engines often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. The Re-ranking of the results reflects the most relevant results for the user and has become very important in current scenario. In this paper, we propose a novel approach for re-ranking of the search results which would be more efficient and effective of a given user as well as the other users. Our approach is to learn the profiles of the users by making use of past browsing histories including queries posed and documents found relevant or irrelevant.