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- W2740256306 abstract "We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for selective search, and is statistically equivalent to exhaustive search even for recall-oriented metrics such as [email protected], an area in which selective search was lacking." @default.
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- W2740256306 date "2017-08-07" @default.
- W2740256306 modified "2023-09-25" @default.
- W2740256306 title "Learning To Rank Resources" @default.
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- W2740256306 doi "https://doi.org/10.1145/3077136.3080657" @default.
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