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- W2023296430 abstract "Combining evidence from multiple retrieval models has been widely studied in the context of of distributed search, metasearch and rank fusion. Much of the prior work has focused on combining retrieval scores (or the rankings) assigned by different retrieval models or ranking algorithms. In this work, we focus on the problem of choosing between retrieval models using performance estimation. We propose modeling the differences in retrieval performance directly by using rank-time features - features that are available to the ranking algorithms - and the retrieval scores assigned by the ranking algorithms. Our experimental results show that when choosing between two rankers, our approach yields significant improvements over the best individual ranker." @default.
- W2023296430 created "2016-06-24" @default.
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- W2023296430 date "2010-07-19" @default.
- W2023296430 modified "2023-10-14" @default.
- W2023296430 title "Learning to select rankers" @default.
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- W2023296430 doi "https://doi.org/10.1145/1835449.1835650" @default.
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