Learning to rank: new approach with the layered multi-population genetic programming on click-through features
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Keyhanipour:2016:GPEM,
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author = "Amir Hosein Keyhanipour and Behzad Moshiri and
Farhad Oroumchian and Maseud Rahgozar and Kambiz Badie",
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title = "Learning to rank: new approach with the layered
multi-population genetic programming on click-through
features",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2016",
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volume = "17",
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number = "3",
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pages = "203--230",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Learning to
rank, Click, through data Layered multi-population
genetic programming",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-016-9263-y",
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size = "28 pages",
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abstract = "Users' click-through data is a valuable source of
information about the performance of Web search
engines, but it is included in few datasets for
learning to rank. In this paper, inspired by the
click-through data model, a novel approach is proposed
for extracting the implicit user feedback from evidence
embedded in benchmarking datasets. This process outputs
a set of new features, named click-through features.
Generated click-through features are used in a layered
multi-population genetic programming framework to find
the best possible ranking functions. The layered
multi-population genetic programming framework is fast
and provides more extensive search capability compared
to the traditional genetic programming approaches. The
performance of the proposed ranking generation
framework is investigated both in the presence and in
the absence of explicit click-through data in the
benchmark datasets. The experimental results show that
click-through features can be efficiently extracted in
both cases but that more effective ranking functions
result when click-through features are generated from
benchmark datasets with explicit click-through data. In
either case, the most noticeable ranking improvements
are achieved at the tops of the provided ranked lists
of results, which are highly targeted by the Web
users.",
- }
Genetic Programming entries for
Amir Hosein Keyhanipour
Behzad Moshiri
Farhad Oroumchian
Maseud Rahgozar
Kambiz Badie
Citations