Evolutionary Rank Aggregation for Recommender Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Oliveira:2016:CEC,
  author =       "Samuel Oliveira and Victor Diniz and 
                 Anisio Lacerda and Gisele L. Pappa",
  title =        "Evolutionary Rank Aggregation for Recommender
                 Systems",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "255--262",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743803",
  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.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Samuel Oliveira Victor Diniz Anisio Lacerda Gisele L Pappa

Citations