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• W2558028811 abstract "Recommender systems are methods built to actively suggest personalized items to users based on their explicit declared preferences (ratings of movies 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 9.5%." @default.
• W2558028811 created "2016-12-08" @default.
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• W2558028811 date "2016-07-01" @default.
• W2558028811 modified "2023-10-14" @default.
• W2558028811 title "Evolutionary rank aggregation for recommender systems" @default.
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• W2558028811 doi "https://doi.org/10.1109/cec.2016.7743803" @default.
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