Abstract: For the popularity prediction of streaming media, a model of popularity prediction based on video characteristics and historical data is proposed. Firstly, according to the video characteristics and the influence in the social network, the popularity of video is predicted using K-Nearest Neighbor(KNN). Subsequently, based on the results of last step, combined with the Autoregressive Moving Average (ARMA) model, the on-demand quantity of the video is predicted using historical data. Finally, the experiment is carried out by crawling the Douban film and Sina microblogging data. The results show that the recall rate of the model is higher than that of the Naive Bayesian classifier, and the average square root error ( RMSE) is decreased by about 20%, compared with the ARMA model.

Key words: media streaming, popularity prediction, K-Nearest Neighbor(KNN), ARMA model