PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Capannini G., Dato D., Lucchese C., Mori M., Nardini F. M., Orlando S., Perego R., Tonellotto N. QuickRank: a C++ suite of learning to rank algorithms. In: IIR 2015 - 6th Italian Information Retrieval Workshop (Cagliari, Italy, 25-26 May 2015). Atti, article n. 10. Paolo Boldi, Reffaele Perego, Fabrizio Sebastiani (eds.). (CEUR Workshop Proceedings, vol. 1404). CEUR-WS.org, 2015.
 
 
Abstract
(English)
Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web col- lections where it involves effectiveness requirements and efficiency con- straints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of efficient and effective Learn- ing 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 flexible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a flexi- ble, extensible and efficient LtR framework to design LtR solutions and fairly compare the performance of different algorithms and ranking mod- els. This paper presents our choices in designing QuickRank and report some preliminary use experiences.
URL: http://ceur-ws.org/Vol-1404/
Subject Learning to rank
Machine learning for Web search
H.3.3 Information Search and Retrieval


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