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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

Algorithms

Industrial research

Learning algorithms

Effective learning

High quality

Learning to rank

Ranking model

Research communities

Training data sets

Web collections

Information retrieval

Publication and Content Type

ref (subject category)

kon (subject category)