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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 351–400 of 753 papers

TitleStatusHype
A Domain Generalization Perspective on Listwise Context Modeling—0
Learning To Rank Diversely At Airbnb—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
Learning to Rank for Blind Image Quality Assessment—0
A Deep Investigation of Deep IR Models—0
Learning to Rank for Expert Search in Digital Libraries of Academic Publications—0
Learning to Rank for Maps at Airbnb—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Learning to Rank for Plausible Plausibility—0
Learning to Rank For Push Notifications Using Pairwise Expected Regret—0
Learning to Rank for Synthesizing Planning Heuristics—0
Learning to rank for uplift modeling—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
Learning to Rank from Samples of Variable Quality—0
Learning to Rank Graph-based Application Objects on Heterogeneous Memories—0
Are Neural Ranking Models Robust?—0
Learning to rank in person re-identification with metric ensembles—0
Learning to Rank Intents in Voice Assistants—0
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Learning to Rank Learning Curves—0
Learning to Rank Lexical Substitutions—0
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
A Passage-Based Approach to Learning to Rank Documents—0
Learning to rank music tracks using triplet loss—0
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation—0
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
Time-Aware Evidence Ranking for Fact-Checking—0
Learning to Rank Personalized Search Results in Professional Networks—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning to Rank Proposals for Object Detection—0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization—0
Answering questions by learning to rank - Learning to rank by answering questions—0
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems—0
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation—0
TopRank: A practical algorithm for online stochastic ranking—0
Learning To Rank Resources with GNN—0
Learning to Rank Retargeted Images—0
Learning to Rank Salient Content for Query-focused Summarization—0
Learning to Rank Scientific Documents from the Crowd—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
TopRank+: A Refinement of TopRank Algorithm—0
Learning to Rank under Multinomial Logit Choice—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Visual Stories From Human Ranking Data—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
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