Evolutionary Rank Aggregation for Recommender Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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