计算机工程 ›› 2017, Vol. 43 ›› Issue (12): 155-159.doi: 10.3969/j.issn.1000-3428.2017.12.029

• 人工智能及识别技术 • 上一篇    下一篇

基于用户行为的改进PageRank影响力算法

王鹏,汪振,李松江,赵建平   

  1. (长春理工大学 计算机科学技术学院,长春 130022)
  • 收稿日期:2016-10-24 出版日期:2017-12-15 发布日期:2017-12-15
  • 作者简介:王鹏(1973—),男,副教授、博士,主研方向为数据挖掘、信息系统;汪振,硕士;李松江,博士;赵建平,教授、博士生导师。
  • 基金项目:
    吉林省科技发展计划重点科技攻关项目(20150204036GX)。

Improved PageRank Influence Algorithm Based on User Behavior

WANG Peng,WANG Zhen,LI Songjiang,ZHAO Jianping   

  1. (School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China)
  • Received:2016-10-24 Online:2017-12-15 Published:2017-12-15

摘要: PageRank算法在计算用户影响力方面只考虑用户间的跟随关系,导致计算结果准确性低下。为此,提出一种将用户行为因素与PageRank算法相结合的URank算法。利用网络中用户发布信息的转发率、评论率以及是否认证等行为因素,综合用户自身质量与追随者质量,得到用户影响力。基于SIR传播模型的实验结果表明,URank算法在计算准确性方面优于PageRank算法。

关键词: 社交网络, 用户影响力, PageRank算法, 用户行为, 传播模型

Abstract: In the calculation of user influence,the PageRank algorithm considers only the following relation among users,which leads to the low accuracy of the calculation results.Therefore,a URank algorithm combining user behavior factors with PageRank algorithm is proposed.By using the factors such as forwarding rate,comment rate and authentication,the user’s quality can be obtained by combining the quality of users and the quality of followers.Experimental results show that based on the SIR propagation model,URank algorithm is superior to PageRank algorithm in computational accuracy.

Key words: social network, user influence, PageRank algorithm, user behavior, propagation model

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