amallia/ltr_cascade

Code repo for the SIGIR '17 paper "Efficient Cost-Aware Cascade Ranking for Multi-Stage Retrieval"

LTR_Cascade

This repository is home to a reference implementation of the cascade ranking model in the SIGIR '17 paper "Efficient Cost-Aware Cascade Ranking for Multi-Stage Retrieval".

If you use this package in your work, please cite the following paper:

@inproceedings{chen_efficient_2017,
 author = {Chen, Ruey-Cheng and Gallagher, Luke and Blanco, Roi and Culpepper, J. Shane},
 title = {Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval},
 booktitle = {Proceedings of {SIGIR} '17},
 year = {2017},
 pages = {445--454},
 publisher = {ACM}
} 

Get Started

To compile our feature processing binaries:

./init-script/build.sh

This will also install/compile all the external dependencies such as Indri and WANDbl. Programs and header files will be installed at external/local/bin.

To install python dependencies:

pip install -r python/requirements.txt

Other bash scripts and Makefiles under experiments should work out of the box.

Ranking Experiments

• Yahoo! Set 1
• Gov2