Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2008/paper/175> ?p ?o. }
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- 175 creator hamed-valizadegan.
- 175 creator hang-li.
- 175 creator rong-jin.
- 175 type InProceedings.
- 175 label "Ranking Refinement and Its Application to Information Retrieval".
- 175 sameAs 175.
- 175 abstract "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., a base ranker) and that obtained from users’ feedbacks. This problem is very important in information retrieval where the 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 insufficient. We present a novel boosting framework for ranking refinement that can effectively leverage the uses of the two sources of information. Our empirical study shows that the proposed algorithm is effective for ranking refinement, and furthermore significantly outperforms the state-of-the-arts ranking algorithms that incorporate the output from the base ranker as an additional feature of instance.".
- 175 hasAuthorList authorList.
- 175 hasTopic World_Wide_Web.
- 175 isPartOf proceedings.
- 175 keyword "Feedback data".
- 175 keyword "Information Retrieval".
- 175 keyword "Learning to Rank".
- 175 title "Ranking Refinement and Its Application to Information Retrieval".