Track: Search: Ranking and Retrieval Enhancement
Ranking Refinement and Its Application to Information Retrieval
- Rong Jin(Michigan State University)
- Hamed Valizadegan(Michigan State University)
- Hang Li(Microsoft Research Asia)
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.
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