Gaussian Mixture Model Based Prediction Method of Movie Rating

被引：0

作者：

Zhu, Jiaxin ^{[1
]}

Guo, Yijun ^{[1
]}

Hao, Jianjun ^{[1
]}

Li, Jianfeng ^{[1
]}

Chen, Duo ^{[2
]}

机构：

[1] Beijing Lab Adv Informat Network, Beijing, Peoples R China

[2] State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China

来源：

2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2016年

关键词：

movie rating; prediction; GMM; data mining;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2114 / 2118

页数：5

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