This repository contains the code for the project Spectral Ranking with Covariates.
You will find implementations of the following ranking algorithms from the following scripts:
src/spektrankers.py
# Our proposed methods
├── SVDRankerNormal (SVDRank)
├── SVDRankerCov (SVDCovRank)
├── SVDRankerKCov (Kernelised SVDCovRank)
├── SerialRank (SerialRank)
├── CSerialRank (C-SerialRank)
├── CCARank (CCRank)
├── KCCARank (Kernelised CCRank)
# Spectral ranking benchmarks
├── RankCentrality (Rank Centrality)
└── DiffusionRankCentrality (Regularised Rank Centrality)
# Probabilistic ranking benchmarks
src/prefkrr.py
└── PreferentialKRR (Bradley Terry with GP link)
src/baselines.py
├── BradleyTerryRanker (Bradley Terry Model)
└── Pairwise_LogisticRegression (Bradley Terry with logistic regressoin)
The algorithms used in this repo came primarily out of the following papers. If you use them in your research we would appreciate a citation to our paper:
@inproceedings{chau2022spectral,
title={Spectral ranking with covariates},
author={Chau, Siu Lun and Cucuringu, Mihai and Sejdinovic, Dino},
booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages={70--86},
year={2022},
organization={Springer}
}