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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 426–450 of 753 papers

TitleStatusHype
Weak Supervision for Improved Precision in Search Systems—0
Web-Scale Responsive Visual Search at Bing—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
What Are You Trying to Do? Semantic Typing of Event Processes—0
What makes you change your mind? An empirical investigation in online group decision-making conversations—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Which Tricks Are Important for Learning to Rank?—0
Whole Page Unbiased Learning to Rank—0
WMRB: Learning to Rank in a Scalable Batch Training Approach—0
Word-Entity Duet Representations for Document Ranking—0
Zeroshot Listwise Learning to Rank Algorithm for Recommendation—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
JPLink: On Linking Jobs to Vocational Interest Types—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models—0
Language Modelling via Learning to Rank—0
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—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
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
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