中文摘要：

目前,对微博转发行为预测主要是对所有微博用户的历史数据进行学习,从而得到转发模型.但是这类模型需要对所有用户的转发行为进行全局预测,存在同质性且无法对具体用户进行个性化预测的缺陷.针对这些问题,提出了基于多任务学习的个性化微博转发行为预测算法.对新浪微博进行了数据抓取、分析和特征选择,根据社会学中影响力的理论,针对微博用户之间进行社交信息交流而导致相互影响的特点,引入了多任务学习方法,以逻辑回归预测模型作为基准算法,将预测模型分为全局模型与个性模型进行学习.预测模型把对每个用户转发行为的预测对应为多个任务,根据微博用户间的社交交互对这些任务进行关联.实验结果表明,所提出的模型能够有效地对单个用户的微博转发行为进行预测,并且提高了转发行为预测的准确率.

英文摘要：

Recently, models for predicting the user retweet behavior are based mainly on the historical retweet data of all users. However, these models are of homogeneity and could not predict a particular user＇s behavior. To overcome these problems, we propose an algorithm for predicting personalized retweet behavior. Based on crawled Weibo data, we have conducted an analysis and a selection of retweet features. According to the influential theory, we introduce the multi-task learning framework to divide the tasks into common global tasks and many individual tasks. Our effective in predicting personalized retweet behavior.