Abstract
Locating interesting information is one of the most important tasks in Information Retrieval (IR). An IR system accepts a query from a user and responds with a set of documents. Generally, the system returns both relevant and non-relevant material and a document organization approach are applied to assist the user in finding the relevant information in the retrieved set. The two most widely used document organization approaches are the ranked list and clustering of the retrieved documents. Both these techniques have their strengths and weaknesses. This paper addresses the problem of offering scalable, adaptive, efficient, full-fledged information retrieval method. We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. We develop a set of techniques for the rank aggregation problem and compare their performance to that of well-known methods. A primary goal of our work is to design rank aggregation techniques for providing robustness of search in the context of web.