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QuickRank :
QuickRank : A C++ suite of learning to rank algorithms
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- Capannini, Gabriele (author)
- Mälardalens högskola,Inbyggda system
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- Dato, D. (author)
- Tiscali S.p.A., Cagliari, Italy
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- Lucchese, C. (author)
- ISTI-CNR, Pisa, Italy
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- Mori, M. (author)
- Tiscali S.p.A., Cagliari, Italy
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- Nardini, F. M. (author)
- ISTI-CNR, Pisa, Italy
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- Orlando, S. (author)
- University Ca' Foscari of Venice, Italy
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- Perego, R. (author)
- ISTI-CNR, Pisa, Italy
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- Tonellotto, N. (author)
- ISTI-CNR, Pisa, Italy
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(creator_code:org_t)
- 2015
- 2015
- English.
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In: CEUR Workshop Proceedings.
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
Close
- 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)
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