ptl2r

Learning to Rank in PyTorch

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This open-source project, referred to as PTL2R (Learning to Rank in PyTorch) aims to provide scalable and extendable implementations of typical learning-to-rank methods based on PyTorch. On one hand, this project enables a uniform comparison over several benchmark datasets leading to an in-depth understanding of previous learning-to-rank methods. On the other hand, this project makes it easy to develop and incorporate newly proposed models, so as to expand the territory of techniques on learning-to-rank.

Test Setting

PyTorch (1.0)

Python (3.6)

Installation

This project is under construction, and is not formally released yet.

Data

MQ2007

MQ2008

MSLR-WEB10K

MSLR-WEB30K

Reference

[1] RankNet: Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd ICML. 89–96.

[2] LambdaRank: Christopher J.C. Burges, Robert Ragno, and Quoc Viet Le. 2006. Learning to Rank with Nonsmooth Cost Functions. In Proceedings of NIPS conference. 193–200.

[3] ListNet: Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to Rank: From Pairwise Approach to Listwise Approach. In Proceedings of the 24th ICML. 129–136.

[4] ListMLE: Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise Approach to Learning to Rank: Theory and Algorithm. In Proceedings of the 25th ICML. 1192–1199.

[5] RankCosine: Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. 2008. Query-level loss functions for information retrieval. Information Processing and Management 44, 2 (2008), 838–855.

[6] AppoxNDCG: Tao Qin, Tie-Yan Liu, and Hang Li. 2010. A general approximation framework for direct optimization of information retrieval measures. Journal of Information Retrieval 13, 4 (2010), 375–397.

[7] WassRank: Hai-Tao Yu, Adam Jatowt, Hideo Joho, Joemon Jose, Xiao Yang and Long Chen. WassRank: Listwise Document Ranking Using Optimal Transport Theory. Proceedings of the 12th International Conference on Web Search and Data Mining (WSDM), 2019.

Community

Slack: PTL2R group

WeChat:

PTL2R

Acknowledgements

This research is partially supported by JSPS KAKENHI Grant Number JP17K12784.

Call for Contribution and/or Collaboration

Anyone who are interested in the following kinds of contributions and/or collaborations are warmly welcomed.

Contribution: Adding one or more implementations of learning-to-rank models based on the current code base.

Collaboration: Joint efforts in developping novel learning-to-rank models.

Relevant Resources

Name Language Deep Learning
PTL2R Python PyTorch
TF-Ranking Python TensorFlow
Shoelace Python Chainer
LEROT Python x
Rank Lib Java x
Propensity SVM^Rank C x
QuickRank C++ x