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http://www.cs.pitt.edu/~valizadegan/Publications/WWW_presentation.pdf

http://www2008.org/papers/pdf/p397-jinA.pdf

We consider the problem of ranking re¯nement, i.e., to

improve the accuracy of an existing ranking function with

a small set of labeled instances. We are, particularly, inter-

ested in learning a better ranking function using two comple-

mentary sources of information, ranking information given

by the existing ranking function (i.e., the base ranker) and

that obtained from users’ feedbacks. This problem is very

important in information retrieval where feedbacks are grad-

ually collected. The key challenge in combining the two

sources of information arises from the fact that the rank-

ing information presented by the base ranker tends to be

imperfect and the ranking information obtained from users’

feedbacks tends to be noisy. We present a novel boosting

algorithm for ranking re¯nement that can e®ectively lever-

age the uses of the two sources of information. Our empiri-

cal study shows that the proposed algorithm is e®ective for

ranking re¯nement, and furthermore it signi¯cantly outper-

forms the baseline algorithms that incorporate the outputs

from the base ranker as an additional feature.