Learning-To-Rank
180 papers with code • 0 benchmarks • 9 datasets
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).
Benchmarks
These leaderboards are used to track progress in Learning-To-Rank
Libraries
Use these libraries to find Learning-To-Rank models and implementationsDatasets
MSLR-WEB10K
Learning to Rank Challenge
ReQA
Most implemented papers
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
We describe a new deep learning architecture for learning to rank question answer pairs.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
List-wise learning to rank methods are considered to be the state-of-the-art.
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
Conversely, simpler models can be optimized on fewer interactions and thus provide a better user experience, but they will converge towards suboptimal rankings.
PRUNE: Preserving Proximity and Global Ranking for Network Embedding
ntumslab/PRUNE
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NeurIPS 2017
We investigate an unsupervised generative approach for network embedding.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
ahangchen/TFusion
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CVPR 2018
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
Unbiased Learning to Rank with Unbiased Propensity Estimation
QingyaoAi/Dual-Learning-Algorithm-for-Unbiased-Learning-to-Rank
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We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.
Learning a Deep Listwise Context Model for Ranking Refinement
QingyaoAi/Deep-Listwise-Context-Model-for-Ranking-Refinement
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Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
HarrieO/RankingComplexLayouts
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Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand.
