Consiglio Nazionale delle Ricerche

Tipo di prodottoBrevetto di invenzione industriale
TitoloA method to rank documents by a computer, using additive ensembles of regression trees and cache optimisation, and search engines using such a method
Anno di deposito2015
Tipologia di brevettoNazionale
Numero brevettoPCT29914
Anno di concessione-
Nazione/i di depositoRoma, Italy
Inventore/iDato D.; Lucchese C.; Nardini F. M.; Orlando S.; Perego R.; Tonellotto N.; Venturini R.
Affiliazioni inventoriTiscali S.p.A., Cagliari, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Università di Venezia, Venezia, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; University of Pisa, Pisa; CNR-ISTI, Pisa, Italy;
Inventori CNR e affiliazioni
  • SALVATORE ORLANDO
  • NICOLA TONELLOTTO
  • CLAUDIO LUCCHESE
  • ROSSANO VENTURINI
  • FRANCO MARIA NARDINI
  • RAFFAELE PEREGO
Lingua/e
  • inglese
DescrizioneLearning-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.
Istituto richiedente-
Titolarità del brevetto-
Parole chiaveLearning to rank, Machine learning for Web search
Link (URL, URI)-
Usi-
Vantaggi-
Note/Altre informazioniCodice Puma: cnr.isti/2015-BV-001 - Il codice modulo commessa corretto è Tecnologie avanzate, Sistemi e Servizi per Grid;
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P09.009.006 : @-SWING Autonomic SoftWare InteNsive Systems for Grid Applications
Progetti Europei-
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