Naïve Bayes regression model and its application in collaborative filtering recommendation algorithm
by Shiqi Wen; Cheng Wang; Haibo Li
International Journal of Internet Manufacturing and Services (IJIMS), Vol. 5, No. 1, 2018

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.

Online publication date: Thu, 22-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Internet Manufacturing and Services (IJIMS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com