计算机与现代化

• 算法分析与设计 • 上一篇    下一篇

一种基于视频特征及历史数据的流行度预测算法

  

  1. (贵州大学计算机科学与技术学院,贵州贵阳550025)
  • 收稿日期:2017-06-05 出版日期:2018-03-08 发布日期:2018-03-09
  • 作者简介:赵命燕(1992-),女,贵州遵义人,贵州大学计算机科学与技术学院硕士研究生,研究方向:计算机与多媒体技术; 李泽平(1964-),男,教授,博士,研究方向:计算机网络与流媒体技术。
  • 基金资助:
    国家自然科学基金资助项目(61462014)

A Popularity Prediction Algorithm Based on Video Characteristics and Historical Data

  1. (College of Computer Science and Technology, Guizhou University, Guiyang 550025, China)
  • Received:2017-06-05 Online:2018-03-08 Published:2018-03-09

摘要: 针对流媒体的流行度预测问题,提出一种基于视频特征及历史数据的流行度预测模型。首先,根据视频特征及在社交网络中的影响力,使用K-近邻(KNN)算法对视频的流行程度进行预测。然后,基于流行程度的预测结果,结合自回归滑动平均(Autoregressive Moving Average,ARMA)模型对视频的点播量进行预测。最后,通过爬取豆瓣电影及新浪微博数据,对模型进行试验。结果表明,与朴素贝叶斯分类器及ARMA模型相比,本文模型的召回率(recall)明显较高,平均平方根误差(RMSE)降低了约20%。

关键词: 流媒体, 流行度预测, KNN算法, ARMA模型

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

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