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 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.