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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 301–325 of 753 papers

TitleStatusHype
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph—0
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
Deep Multi-view Learning to Rank—0
Deep Neural Network for Learning to Rank Query-Text Pairs—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
Learning from User Interactions with Rankings: A Unification of the Field—0
Individually Fair Ranking—0
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions—0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
Learning Modulo Theories for preference elicitation in hybrid domains—0
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Paraphrasing for Multiword Expressions—0
Learning Rank Functionals: An Empirical Study—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Learning Term Weights for Ad-hoc Retrieval—0
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval—0
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