PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Nardini F. M., Orlando S., Perego R., Silvestri F., Trani S. Improve ranking efficiency by optimizing tree ensembles. In: IIR 2016 - 7th Italian Information Retrieval Workshop (Venezia, Italia, 30-31 May 2016). Abstract, article n. 27. Giorgio Maria Di Nunzio, Franco Maria Nardini, Salvatore Orlando (eds.). (CEUR Workshop Proceedings, vol. 1653). CEUR-WS.org, 2016.
 
 
Abstract
(English)
Learning to Rank (LtR) is the machine learning method of choice for producing highly effective ranking functions. However, effi- ciency and effectiveness are two competing forces and trading off effectiveness 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.
URL: http://ceur-ws.org/Vol-1653/paper_27.pdf
Subject Learning to Rank
Efficiency
Pruning
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval


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