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 that can effectively
combat "spam," a serious problem in Web searches. Experiments show that
our methods are simple, efficient, and effective.
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