Title: Naïve Bayes regression model and its application in collaborative filtering recommendation algorithm

Authors: Shiqi Wen; Cheng Wang; Haibo Li

Addresses: College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China ' College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; The Xiamen Engineering Research Centre of Enterprise Interoperability and Business Intelligence, China ' College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; The Xiamen Engineering Research Centre of Enterprise Interoperability and Business Intelligence, China

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.

Keywords: Naïve Bayes regression model; discretisation; expectation; model-based collaborative filtering; time efficiency; recommendation accuracy.

DOI: 10.1504/IJIMS.2018.090594

International Journal of Internet Manufacturing and Services, 2018 Vol.5 No.1, pp.85 - 99

Received: 02 Aug 2017
Accepted: 30 Oct 2017

Published online: 19 Mar 2018 *

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