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- W4256179948 abstract "Aiming at the problem that the general Bayes classification model cannot be applied to regression prediction, a Naïve Bayes regression model is proposed. Firstly, in order to simplify the complexity of model, attribute values and decision values are discretised from continuous value to discrete value. By summing the probability of Bayesian classification and using the mathematical expectation value as the regression value, the process of regression problem is turned into a classification problem and the Naive Bayes classification model was modified into Bayesian regression model. The difference of input attribute values, output values, application scope, requirement of output values, operation to obtain output values between Naïve Bayes classification model and Naïve Bayes regression model is compared in detailed. Secondly, the Naïve Bayes regression model is application in collaborative filtering recommendation. This study identifies the user and the item as independent attribute characteristics, while rating as the classification category. And the attribute values and category values are discretised to simplify the complexity of Naïve Bayes regression model. The experiment results on Movielens-100k, Eachmovie and Jester data set show that this new method has high success rate and efficiency." @default.
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- W4256179948 date "2018-01-01" @default.
- W4256179948 modified "2023-10-16" @default.
- W4256179948 title "Naïve Bayes regression model and its application in collaborative filtering recommendation algorithm" @default.
- W4256179948 doi "https://doi.org/10.1504/ijims.2018.090594" @default.
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