Improve ranking efficiency by optimizing tree ensembles
Lucchese C., Nardini F. M., Orlando S., Perego R., Silvestri F., Trani S.
Learning to Rank Efficiency Pruning
Learning to Rank (LtR) is the machine learning method of choice for producing highly effective ranking functions. However, efficiency and effectiveness are two competing forces and trading off effiectiveness for meeting efficiency constraints typical of production systems is one of the most urgent issues. This extended abstract shortly summarizes the work in [4] proposing CLEaVER, a new framework for optimizing LtR models based on ensembles of regression trees. We summarize the results of a comprehensive evaluation showing that CLEaVER is able to prune up to 80% of the trees and provides an efficiency speed-up up to 2:6x without affecting the effectiveness of the model.
Source: 7th Italian Information Retrieval Workshop, Venezia, Italia, 30-31 May 2016
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@inproceedings{oai:it.cnr:prodotti:366631,
title = {Improve ranking efficiency by optimizing tree ensembles},
author = {Lucchese C. and Nardini F. M. and Orlando S. and Perego R. and Silvestri F. and Trani S.},
booktitle = {7th Italian Information Retrieval Workshop, Venezia, Italia, 30-31 May 2016},
year = {2016}
}
0000-0002-2545-0425