The repo features the experimental analysis on Contextual Combinatorial Cascading Bandits. We highly recommend cite the the paper, using the following bib
@inproceedings{li2016contextual,
title={Contextual Combinatorial Cascading Bandits},
author={Li, Shuai and Wang, Baoxiang and Zhang, Shengyu and Chen, Wei},
booktitle={Proceedings of The 33rd International Conference on Machine Learning},
pages={1245--1253},
year={2016}
}
- python3.5
- numpy
- scipy, with community/atlas-lapack-base
- matplotlib, with cairocffi
- colorama
- Movielens
wget http://files.grouplens.org/datasets/movielens/ml-20m.zip
unzip ml-20m.zip -d movielens
- Rocketfuel
wget http://research.cs.washington.edu/networking/rocketfuel/maps/rocketfuel_maps_cch.tar.gz
wget http://research.cs.washington.edu/networking/rocketfuel/maps/weights-dist.tar.gz
wget http://research.cs.washington.edu/networking/rocketfuel/maps/rocketfuel-traces.tgz
tar -xvf rocketfuel_maps_cch.tar.gz -C isp
tar -xvf weights-dist.tar.gz -C isp-weight
tar -xvf rocketfuel-traces.tgz -C isp-trace
- Before running, please modify the code bleedtest.py accordingly, then
python3 bleedtest.py
- Logs in human-understandable format are attached in log