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• W1036229589 abstract "The construction of rankings consists of ordering retrieved results according to certain criteria. Rankings can provide relevant information to analysts from different sectors of industry. For the music industry, rankings enable understanding how musical genres and popularity of artists and their songs evolve over time, allowing analyses of history data and trends. Due to the importance of building rankings in the musical scope, data mining techniques have been used to predict rankings by using information from social media. This work evaluates regression models for prediction of artists' rankings using historical data (daily rankings of artists) extracted from website Vagalume. Three regression techniques (k-Nearest Neighbors - k-NN, Multiple Linear Regression - MLR and Random Forests - RF) were evaluated in this study considering different scenarios. Results obtained from experiments showed that predictions with low error rates can be obtained, indicating that data mining techniques can be used to obtain information to assist the music industry in decision making." @default.
• W1036229589 created "2016-06-24" @default.
• W1036229589 creator A5042325909 @default.
• W1036229589 creator A5045607351 @default.
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• W1036229589 date "2015-05-26" @default.
• W1036229589 modified "2023-09-25" @default.
• W1036229589 title "Prediction of Artists' Rankings by Regression" @default.
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• W1036229589 doi "https://doi.org/10.5555/2814058.2814076" @default.
• W1036229589 hasPublicationYear "2015" @default.
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