Evolutionary Rank Aggregation for Recommender Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Oliveira:2016:CEC,
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author = "Samuel Oliveira and Victor Diniz and
Anisio Lacerda and Gisele L. Pappa",
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title = "Evolutionary Rank Aggregation for Recommender
Systems",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "255--262",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7743803",
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abstract = "Recommender systems are methods built to actively
suggest personalized items to users based on their
explicit declared preferences (ratings of feature films
in Netflix), or implicitly observed actions (purchase
history). Although a great number of recommendation
methods have been previously proposed in the
literature, in many problems these methods present a
high degree of disagreement in their recommendations.
In this scenario, rank aggregation methods are an
interesting solution. They can help finding a consensus
on which items should be recommended to the user by
taking into account the opinion of all available
methods. In this direction, this paper proposes ERA
(Evolutionary Rank Aggregation), a genetic programming
method that outputs an aggregated ranking function
built from information extracted from individual input
rankings. ERA was tested in four large scale datasets,
and obtained better results than other rank aggregation
methods in three datasets, improving the results of
mean average ranking precision in up to 9.5percent.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Samuel Oliveira
Victor Diniz
Anisio Lacerda
Gisele L Pappa
Citations