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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 201–210 of 753 papers

TitleStatusHype
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions—0
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Adversarial Attacks on Online Learning to Rank with Click Feedback—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to RankCode0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Unconfounded Propensity Estimation for Unbiased Ranking—0
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