多任务回归在社交媒体挖掘中的应用

Application of multi-task regression in social media mining

随着社交媒体的迅速发展,针对网络信息挖掘的研究成为互联网领域备受关注的研究热点之一.传统的单任务回归对各个任务分别建模,在多变量预测的场合中,无法合理利用变量之间的共享信息.因此,本文通过多任务回归网络挖掘方法,分析社交媒体用户人格和网络行为的关联模式.实验通过在线被试邀请,采集了335个人人网用户样本和563个新浪微博用户样本.采用多任务回归的算法,预测精度可达87%以上.实验结果表明多任务回归对多变量建模效果要优于单任务学习算法.

With the development of Social Media,web mining analysis has been regarded as one of hot research topics.Traditional single task regression builds models for each task,which ignores the sharing information among tasks in the occasion of multi-variable prediction.Therefore,this paper used multi-task regression mining method,and managed to analyze the pattern between user ’s personality and network behavior.This study collected a sample set of 335 RenRen users and 563 Weibo users through online test invitation.Using multi-task regression,the final prediction accuracy is 87% or more.The result means that multi-task regression works better then single task regression for multi-variable modeling.