This is the home page of the “Efficiency/Effectiveness Trade-offs in Learning to Rank” tutorial by Claudio Lucchese and Franco Maria Nardini at the 3rd ACM International Conference on Theory of Information Retrieval (ICTIR) 2017.
email: ltrtutorial [AT] isti.cnr.it
General Software Requirements
To ease the use of the material we will supply during the tutorial we strongly encourage you to install the following software:
- GCC 5.0 or above.
- CMake 2.9 or above.
- Python 2.7 (with Ipython and Jupyter notebook)
- QuickRank (open source, instructions to install it can be found here)
- RankEval (open source, instructions to install it can be found here)
Specific Software Requirements for QuickScorer
QuickScorer is the state-of-the-art algorithm for scoring forests of regression trees. QuickScorer is undergoing a patent process. The source code of QuickScorer is made available under NDA with Tiscali S.p.A. The attendees of the tutorial can access the source code of QuickScorer by signing this NDA. After receiving a signed copy of the NDA we will send you the source code of QuickScorer.
The software requirements needed to compile and run QuickScorer are:
- GCC 5.0 or above.
- CMake 2.9 or above.
- Boost 1.60 or above.
Useful Software
Our HandsOn sessions use also:
Resources