Online Popularity Prediction Scheme Based on Converging Property in Content Aware Soft Real Time Media Broadcast System

Huangqing Chen; Xiaofeng Zhong; Jian Sun; Jing Wang

doi:10.11959/j.issn.1000-0801.2015221

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Online Popularity Prediction Scheme Based on Converging Property in Content Aware Soft Real Time Media Broadcast System

research and development | 更新时间：2024-06-05

- Online Popularity Prediction Scheme Based on Converging Property in Content Aware Soft Real Time Media Broadcast System
- Telecommunications Science Vol. 31, Issue 11, Pages: 1-6(2015)
- 作者机构：
  
  1. 清华大学信息科学与技术国家实验室北京 100084
  2. 清华大学电子工程系北京 100084
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2015221
  CLC：
- Published Online：2015-11，
  
  Published：20 November 2015
- 稿件说明：
移动端阅览
Huangqing Chen, Xiaofeng Zhong, Jian Sun, et al. Online Popularity Prediction Scheme Based on Converging Property in Content Aware Soft Real Time Media Broadcast System[J]. Telecommunications science, 2015, 31(11): 1-6.
DOI：

Huangqing Chen, Xiaofeng Zhong, Jian Sun, et al. Online Popularity Prediction Scheme Based on Converging Property in Content Aware Soft Real Time Media Broadcast System[J]. Telecommunications science, 2015, 31(11): 1-6. DOI： 10.11959/j.issn.1000-0801.2015221.

摘要

少数在线热门内容会在短时间内吸引大量用户的访问，并占用大量的网络传输资源。如果能预知内容的热门程度（即流行度）并将热门内容广播给潜在用户，将极大地节省网络传输资源，这正是CASoRT 系统的主要功能。通过对国内商业蜂窝通信系统中收集的相关数据进行分析和研究，发现在用户行为、地理位置、数据内容等方面存在明显的聚集特性。根据上述特性给出了两个流行度预测算法，即对数线性和恒定比例模型，并使用最优观察门限改善两算法的性能。通过对两算法仿真结果的比较，对数线性模型表现更优，被选作系统的在线流行度预测方法。

Abstract

A small number of online popular contents are often clicked by a great quantity of users in a short period

and take the most of the wireless cellular network traffic.With popularity prediction

the popular contents would be broadcasted to the potential users for saving a lot of transmitting resource

as illustrated in content aware soft real time media broadcast（CASoRT）system.With the data set collected from the Chinese commercial cellular network

the converging property of web contents

users and geographic positions in online news was shown.Then

two prediction schemes such as linear log and constant scaling model were proposed to estimate the popularity of online news

and improved by an optimal observation threshold.After comparison of simulation results

the linear log model performs better.

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references

Zhong X F , Zhao M , Zhou S D , et al . Content aware soft real time media broadcast （casort） . Proceedings of the Third International Conference on Communications and Networking , Hangzhou,China , 2008

Tatar A , Leguay J , Antoniadis P , et al . Predicting the popularity of online articles based on user comments . Proceedings of the International Conference on Web Intelligence,Mining and Semantics , Sogndal,Norway , 2011

Tsagkias M , Weerkamp W , De R M . News comments:exploring,modeling and online prediction . Proceedings of the 32nd European Conference on Advances in Information Retrieval （ECIR2010） , Berlin,Germany , 2010

Kaltenbrunner A , Gomez V , Lopez V . Description and prediction of slashdot activity . Proceedings of the 5th Latin American Web Congress , Santiago,Chile , 2007

Szabo G , Huberman B A . Predicting the popularity of online content . Communications of the ACM , 2010 , 53 （ 8 ）: 80 ～ 88

Sun J , Zhong X , Zhou X , et al . Recommendation scheme based on converging properties for contents broadcasting . Proceedings of the 11th International Conference on Natural Computation , Anaheim,California,USA , 2015

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Neural network and Markov based combination prediction algorithm of video popularity

Related Author

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Related Institution

School of Computer and Information, Hefei University of Technology

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