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QuickRank: A C++ suite of learning to rank algorithms
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Tiscali S.p.A., Cagliari, Italy.
ISTI-CNR, Pisa, Italy.
Tiscali S.p.A., Cagliari, Italy.
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2015 (English)In: CEUR Workshop Proceedings, 2015, Vol. 1404Conference paper, Published paper (Refereed)
Abstract [en]

Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and effciency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of effcient and effective Learning to Rank (LtR) algorithms that allows high-quality ranking functions to be devised from possibly huge training datasets. QuickRank is a project with a double goal: i) answering industrial need of Tiscali S.p.A. for a exible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a exible, extensible and effcient LtR framework to design LtR solutions and fairly compare the performance of different algorithms and ranking models. This paper presents our choices in designing QuickRank and report some preliminary use experiences.

Place, publisher, year, edition, pages
2015. Vol. 1404
Keywords [en]
Algorithms, Industrial research, Learning algorithms, Effective learning, High quality, Learning to rank, Ranking model, Research communities, Training data sets, Web collections, Information retrieval
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-28726Scopus ID: 2-s2.0-84938522405OAI: oai:DiVA.org:mdh-28726DiVA, id: diva2:847820
Conference
6th Italian Information Retrieval Workshop, IIR 2015, 25 May 2015 through 26 May 2015
Available from: 2015-08-21 Created: 2015-08-21 Last updated: 2015-08-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • asciidoc
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