Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. Unfortunately, the computational cost of these ranking models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QUICKSCORER, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache-aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to-Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.

QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees

LUCCHESE, Claudio;ORLANDO, Salvatore;
2015-01-01

Abstract

Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. Unfortunately, the computational cost of these ranking models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QUICKSCORER, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache-aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to-Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.
2015
SIGIR '15 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
File in questo prodotto:
File Dimensione Formato  
QuickScorer- A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees.pdf

non disponibili

Tipologia: Versione dell'editore
Licenza: Licenza non definita
Dimensione 1.47 MB
Formato Adobe PDF
1.47 MB Adobe PDF   Visualizza/Apri
paper.pdf

accesso aperto

Descrizione: post-print (Editore ACM: green open access)
Tipologia: Documento in Post-print
Licenza: Accesso libero (no vincoli)
Dimensione 1.83 MB
Formato Adobe PDF
1.83 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3661259
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 57
  • ???jsp.display-item.citation.isi??? 47
social impact