基于动态演化的讨论帖流行度预测

互联网用户间的交互行为，使得某些用户生成的内容(如讨论帖、微博话题)变得流行。对所关注内容的流行度进行建模和预测，在多个领域中具有十分重要的研究和应用价值。针对论坛讨论帖的流行度预测问题，基于早期的发展演化信息，探讨了影响讨论帖流行度的相关动态因素，并提出一种结合局部特性、融合多个动态因素的讨论帖流行度预测算法。以豆瓣小组的数据为例，对所提出的算法进行实验。实验结果表明，所提出的融合多种动态因素的方法与基准方法相比，能够较好地预测讨论帖的流行度。

Predicting Popularity of Forum Threads Based on Dynamic Evolution

Web user's online interacting behavior with others usually makes some user generated content (e.g. forum threads and Weibo topics) popular. The modeling and prediction of the popularity of online content are of great research importance and practical value in many different domains. To predict the popularity of forum threads, this paper discusses several dynamic factors that might affect the popularity of online content based on the information of dynamic evolution at the early stage, and proposes a popularity prediction algorithm which makes use of the locality property and combines multiple dynamic factors. The proposed algorithm is further evaluated with the Douban group dataset. The experimental results show that, compared with the baseline methods, our method achieves relatively better performance in predicting the popularity of forum threads.