Trend prediction of internet public opinion based on collaborative filtering

Collaborative filtering recommendation has very important applications in the personalized recommendation. Especially it is widely used in e-commerce. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations and provide a lo...

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Bibliographic Details
Published in2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) pp. 583 - 588
Main Authors Xuegang Chen, Mingna Xia, Jieren Cheng, Xiangyan Tang, Jialu Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2016
Subjects
Collaboration
collaborative filtering
Filtering
Filtering algorithms
Internet
internet public opinion
prediction
Prediction algorithms
Predictive models
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Summary:Collaborative filtering recommendation has very important applications in the personalized recommendation. Especially it is widely used in e-commerce. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations and provide a lot similar or interesting advice for users. The method of internet public opinion trend prediction based on collaborative filtering is proposed in order to solve the problem of internet public opinion trend prediction. This paper introduces the collaborative filtering algorithm and study user-based collaborative filtering algorithm, then the principles of internet public opinion trend prediction based on collaborative filtering are analyzed, and the frame structure of internet public opinion trend prediction is designed. Furthermore, a series of experimental results show that this method can effectively predict the development trend of internet public opinion.
DOI:10.1109/FSKD.2016.7603238