111 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).


Use these libraries to find Learning-To-Rank models and implementations

Most implemented papers

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

tensorflow/recommenders 19 Aug 2020

Learning effective feature crosses is the key behind building recommender systems.

Improving Pairwise Ranking for Multi-label Image Classification

raingo-ur/mll-tf CVPR 2017

Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks.

Introducing LETOR 4.0 Datasets

wildltr/ptranking 9 Jun 2013

We call the two query sets MQ2007 and MQ2008 for short.

Support vector comparison machines

tdhock/rankSVMcompare 30 Jan 2014

In ranking problems, the goal is to learn a ranking function from labeled pairs of input points.

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

david-yoon/QA_HRDE_LTC NAACL 2018

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.

Learning Latent Vector Spaces for Product Search

cvangysel/SERT 25 Aug 2016

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

xialeiliu/CrowdCountingCVPR18 CVPR 2018

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

tensorflow/ranking 11 Nov 2018

To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

xialeiliu/RankIQA 17 Feb 2019

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

HarrieO/OnlineLearningToRank 15 Jul 2019

At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.