基于移动社交环境的用户行为最优关联预测

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.

Optimal Correlation Prediction of User Behaviors Based on Mobile Social Environment

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;par-ticularly, the representativeness degree consists of Kendall coefficient based similarity degree and interac-tion statistics based interaction degree. Second, by using Apriori theory, the correlation analyses for tar-get 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 opti-mally and predict user next behaviors accurately. Extensive simulation results verify the effectiveness of proposed algorithm.