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 with no code
ALEXR: An Optimal Single-Loop Algorithm for Convex Finite-Sum Coupled Compositional Stochastic Optimization
This paper revisits a class of convex Finite-Sum Coupled Compositional Stochastic Optimization (cFCCO) problems with many applications, including group distributionally robust optimization (GDRO), learning with imbalanced data, reinforcement learning, and learning to rank.
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.
SortNet: Learning To Rank By a Neural-Based Sorting Algorithm
Two main approaches exist in literature for the task of learning to rank: 1) a score function, learned by examples, which evaluates the properties of each object yielding an absolute relevance value that can be used to order the objects or 2) a pairwise approach, where a "preference function" is learned using pairs of objects to define which one has to be ranked first.
Unbiased Offline Evaluation for Learning to Rank with Business Rules
For industrial learning-to-rank (LTR) systems, it is common that the output of a ranking model is modified, either as a results of post-processing logic that enforces business requirements, or as a result of unforeseen design flaws or bugs present in real-world production systems.
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization
To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition.
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank
Existing systems for biomedical named entity normalization heavily rely on dictionaries, manually created rules, and high-quality representative features such as lexical or morphological characteristics.
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank
As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively.
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition
This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O).
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain
We replicate prior work on ranking domain-specific synonyms in the consumer health domain by applying the approach to a new language and domain: identifying Swedish language synonyms in the building construction domain.
