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.
Improve ranking efficiency by optimizing tree ensembles / Lucchese, C.; Nardini, F. M.; Orlando, S.; Perego, R.; Silvestri, F.; Trani, S.. - 1653:(2016). (Intervento presentato al convegno 7th Italian Information Retrieval Workshop, IIR 2016 tenutosi a Venezia; Italia).
Improve ranking efficiency by optimizing tree ensembles
Orlando S.;Silvestri F.
;
2016
Abstract
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.| File | Dimensione | Formato | |
|---|---|---|---|
|
Lucchese_Improve-Ranking-Efficiency_2016.pdf
accesso aperto
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Creative commons
Dimensione
159.39 kB
Formato
Adobe PDF
|
159.39 kB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
