Gaussian Mixture Model based prediction method of movie rating

Abstract

Nowadays, with the increasing usage of the internet, the movie ratings on the SNS website related to movies influence our choice of movies remarkably. However, a newly released film has insufficient rating counts to reflect the quality of the movie, and it can not avoid the influence of malicious rating by some people. Therefore, this paper proposes a method of rating prediction based on Gaussian Mixture Model (GMM), enabled by imitating rating behavior of audience. Meanwhile, this model can avoid the influence of malicious rating because GMM is not sensitive to exception. In GMM, 4 features of the movies are taken into consideration. In order to verify the validity of our model, data from Douban website is used in the implementation. Experimental results exhibit the effectiveness of the method and an improved performance of rating prediction is achieved compared with the benchmark of linear regression.