QuickRank: a C++ Suite of Learning to Rank Algorithms

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.

Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves eectiveness requirements and eciency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of ecient and eective 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 ecient LtR framework to design LtR solutions and fairly compare the performance of dierent algorithms and ranking models. This paper presents our choices in designing QuickRank and report some preliminary use experiences.