基于移动社交环境的用户行为最优关联预测

doi:10.13190/j.jbupt.2017-141

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (6): 50-56.doi: 10.13190/j.jbupt.2017-141

基于移动社交环境的用户行为最优关联预测

张晖^1,2,3, 王敏¹

1. 南京邮电大学江苏省无线通信重点实验室, 南京 210003;
2. 南京邮电大学通信与网络技术国家工程研究中心, 南京 210003;
3. 苏州大学江苏省计算机信息处理技术重点实验室, 江苏苏州 215006

收稿日期:2017-07-11 出版日期:2017-12-28 发布日期:2017-12-28
作者简介:张晖(1982-),男,博士,副教授,硕士生导师,E-mail:zhhjoice@126.com.
基金资助:
国家自然科学基金项目（61471203）；江苏省"六大人才高峰"项目（RJFW-024）；江苏省"青蓝工程"项目（2016）；南京邮电大学"1311"人才计划项目（2015）；通信与网络技术国家工程研究中心开放课题项目（TXKY17002）；江苏省计算机信息处理技术重点实验室开放课题项目（KJS1518）；国家科技重大专项项目（2012ZX03001008-003）

Optimal Correlation Prediction of User Behaviors Based on Mobile Social Environment

ZHANG Hui^1,2,3, WANG Min¹

1. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2. National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China

Received:2017-07-11 Online:2017-12-28 Published:2017-12-28
Supported by:

摘要/Abstract

摘要： 提出了一种基于移动社交环境的用户行为多重分析与最优预测算法.首先，针对目标用户所属的各个社交群组，分别建立基于代表度的最优化模型，选择出任一社交群组内最具代表的关联用户，以分析目标用户在不同社会属性下的业务行为；特别地，代表度由基于Kendall系数的相似度和基于交互统计的交互度联合构成；其次，借助Apriori理论分别对目标用户和各最具代表的关联用户进行关联分析，并提出基于最小二乘模型的加权融合方法，以最优地融合上述关联分析结果且实现用户行为的精准预测.仿真结果验证了该算法的有效性.

关键词: 移动社交环境, 行为分析, 行为预测, 关联分析, 最小二乘模型

Abstract: A multiple analysis and optimal prediction algorithm of user behaviors based on mobile social environment is proposed. First, for each social group of a target user, an optimization model based on representativeness degree is formulated to select the most representative correlated user from this social group for analyzing the service behaviors of target user caused by the corresponding social attribute; particularly, the representativeness degree consists of Kendall coefficient based similarity degree and interaction statistics based interaction degree. Second, by using Apriori theory, the correlation analyses for target user and its most representative correlated users are performed respectively, and then a least-square model based weighted fusion method is presented to integrate the above correlation analysis results optimally and predict user next behaviors accurately. Extensive simulation results verify the effectiveness of proposed algorithm.

Key words: mobile social environment, behavior analysis, behavior prediction, correlation analysis, least square model

中图分类号:

TP181

张晖, 王敏. 基于移动社交环境的用户行为最优关联预测[J]. 北京邮电大学学报, 2017, 40(6): 50-56.

ZHANG Hui, WANG Min. Optimal Correlation Prediction of User Behaviors Based on Mobile Social Environment[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(6): 50-56.

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基于移动社交环境的用户行为最优关联预测

Optimal Correlation Prediction of User Behaviors Based on Mobile Social Environment

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