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miscellaneous-6

Learning-To-Rank

3260 papers • 126 benchmarks • 313 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).

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Benchmarks

These leaderboards are used to track progress in learning-to-rank-12

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find learning-to-rank-12 models and implementations

allegro/allRank

3 papers 793

Datasets

MSLR-WEB10K

MQ2007

MQ2008

Learning to Rank Challenge

Learning to Rank Challenge

MultiFC

ReQA

Subtasks

No subtasks available.

Most implemented papers

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

Lichan Hong, Ruoxi Wang, Ed H. Chi, Rakesh Shivanna, D. Cheng, Sagar Jain, Dong Lin•Tue Aug 18 2020

This work proposes an improved framework DCN-V2, which is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems at Google.

715

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Guzpenha/transformer_rankers

2 papers 158

stanojevic/beer

2 papers 38

ART Dataset

ART Dataset

Flickr Cropping Dataset

Flickr Cropping Dataset

REFreSD

0

Improving Pairwise Ranking for Multi-label Image Classification

Jiebo Luo, Yale Song, Y. Li•Mon Apr 10 2017

A novel loss function for pairwise ranking is proposed, which is smooth everywhere, and a label decision module is incorporated into the model, estimating the optimal confidence thresholds for each visual concept.

224 0

Introducing LETOR 4.0 Datasets

Tao Qin, Tie-Yan Liu•Sat Jun 08 2013

LETOR is a package of benchmark data sets for research on LEarning TO Rank, which contains standard features, relevance judgments, data partitioning, evaluation tools, and several baselines.

429 0

Support vector comparison machines

T. Hocking, Supaporn Spanurattana, Masashi Sugiyama•Wed Jan 29 2014

This paper uses simulated nonlinear patterns, a real learning to rank sushi data set, and a chess data set to show that the proposed SVMcompare algorithm outperforms SVMrank when there are equality pairs.

1 0

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

Joongbo Shin, Seunghyun Yoon, Kyomin Jung•Mon Oct 09 2017

A novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module is proposed, that shows state-of-the-art results for ranking question-answer pairs.

35 0

Learning Latent Vector Spaces for Product Search

E. Kanoulas, M. de Rijke, Christophe Van Gysel•Wed Aug 24 2016

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 and achieves its enhanced performance as it learns better product representations.

144 0

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

W. Bruce Croft, Yongfeng Zhang, Qingyao Ai, Xu Chen•Sun Nov 05 2017

This work analyzes how information propagates among different information sources in a gradient-descent learning paradigm, and proposes an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice.

324 0

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

Michael Bendersky, Marc Najork, Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi•Sat Nov 10 2018

This work proposes a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list, and refers to this framework as GSFs---groupwise scoring functions.

106 0

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

Joost van de Weijer, Andrew D. Bagdanov, Xialei Liu•Sat Feb 16 2019

The 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.

183 0

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

R. Jagerman, Harrie Oosterhuis, M. de Rijke•Sun Jul 14 2019

The results show that the choice between the methodologies is consequential and depends on the presence of selection bias, and the degree of position bias and interaction noise, and that counterfactual methods can obtain the highest ranking performance; however, in other circumstances their optimization can be detrimental to the user experience.

77 0

Adding a benchmark result helps the community track progress.

Learning-To-Rank | State-of-the-Art