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 efficiency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of efficient 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 flexible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a flexible, extensible and efficient 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.