Rank aggregation methods for the web

We consider the problem of combining ranking results from various sources. In the context of the Web, the main ap-plications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word asso-ciations. We develop a set of techniques for the rank aggre-gation problem and compare their performance to that of well-known methods. A primary goal of our work is to de-sign rank aggregation techniques that can effectively combat "spam," a serious problem in Web searches. Experiments show that our methods are simple, efficient, and effective.