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Predicting Micro-blog Individual Retweet Behavior Based on User's Interest Drift

机译：基于用户兴趣漂移的微博个人转推行为预测

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摘要

Micro-blog post retweet prediction is one of the key technologies for researching information dissemination. The factors that affect retweet are multifaceted; However, the existing methods mainly focus on the research of micro-blog post information attributes and network structure characteristics, and do not fully consider the interests of users that are changing with time. To address this issue, we firstly propose some new methods of measuring features, which are user's influence, user's intimacy with named entities and users' intimacy. Meanwhile, in order to solve this problem of user' s interest changing, we propose a topic model based on user's interest drift to measure user's current interest and calculate topic similarity between users. Finally, we establish a novel micro-blog prediction retweet model based on above features. Our experimental results demonstrate that our proposed method has superior accuracy and stability, compared with several classification methods.
机译：微博发布转推预测是研究信息传播的关键技术之一。影响转推的因素是多方面的;但是，现有的方法主要集中在微博帖子信息属性和网络结构特征的研究上，并未充分考虑随时间变化的用户的兴趣。为了解决这个问题，我们首先提出了一些新的度量特征的方法，即用户的影响力，用户与命名实体的亲密性以及用户的亲密性。同时，为了解决用户兴趣变化的问题，提出了一种基于用户兴趣漂移的主题模型，用于度量用户当前的兴趣并计算用户之间的主题相似度。最后，基于上述特征，我们建立了一个新颖的微博预测转推模型。我们的实验结果表明，与几种分类方法相比，我们提出的方法具有更高的准确性和稳定性。

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