Learning-To-Rank
178 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
Latest papers
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
naver/sardine
•
•
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
GLEN: Generative Retrieval via Lexical Index Learning
skleee/GLEN
•
•
For training, GLEN effectively exploits a dynamic lexical identifier using a two-phase index learning strategy, enabling it to learn meaningful lexical identifiers and relevance signals between queries and documents.
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Active Data Manipulation
Federated learning (FL) has recently emerged as a privacy-preserving approach for machine learning in domains that rely on user interactions, particularly recommender systems (RS) and online learning to rank (OLTR).
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
Using this dataset, we train a neural context retriever based on a BERT model that is able to find relevant context for NER.
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness
Previous works have proposed efficient algorithms to train stochastic ranking models that achieve fairness of exposure to the groups ex-ante (or, in expectation), which may not guarantee representation fairness to the groups ex-post, that is, after realizing a ranking from the stochastic ranking model.
Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
kominaru/brie
•
•
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens.
A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
Empirical insights from a large-scale social media platform show how our probabilistic position bias model more accurately captures empirical exposure than existing models, and paves the way for unbiased evaluation and learning-to-rank.
MIST-CF: Chemical formula inference from tandem mass spectra
Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction.
Learning to Rank in Generative Retrieval
However, only learning to generate is insufficient for generative retrieval.
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
xk-huang/OrdinalCLIP
•
•
NeurIPS 2023
Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment.
