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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 376–400 of 753 papers

TitleStatusHype
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
Time-Aware Evidence Ranking for Fact-Checking—0
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems—0
TopRank: A practical algorithm for online stochastic ranking—0
TopRank+: A Refinement of TopRank Algorithm—0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank—0
Towards Constructing Sports News from Live Text Commentary—0
Towards Deep and Representation Learning for Talent Search at LinkedIn—0
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank—0
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models—0
Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study—0
Towards Non-Parametric Learning to Rank—0
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback—0
Towards Productionizing Subjective Search Systems—0
Towards Theoretical Understanding of Weak Supervision for Information Retrieval—0
Towards Two-Stage Counterfactual Learning to Rank—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers—0
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