Ranking Refinement and Its Application to Information Retrieval
(0) We consider the problem of ranking refinement, I.e., to improve the accuracy of an existing ranking function with a small set of labeled instances. We are, particularly, interested in learning a better ranking function using two complementary 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 gradually collected. The key challenge in combining the two sources of information arises from the fact that the ranking 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 refinement that can e.ectively leverage the uses of the two sources of information. Our empirical study shows that the proposed algorithm is e.ective for ranking refinement, and furthermore it significantly outperforms the baseline algorithms that incorporate the outputs from the base ranker as an additional feature.
Learning to Rank Background Information Boosting Incremental Learning
Rong Jin Hamed Valizadegan Hang Li
Computer Science and Engineering Michigan State University East Lansing, MI 48824 Microsoft Research Asia 4F, Sigma Center No.49 Zhichun Road, Haidian Beijing, 100080, China
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）