5G-Enabled Cooperative Intelligent Vehicular (5GenCIV) Framework: When Benz Meets Marconi
5G-Enabled Cooperative Intelligent Vehicular (5GenCIV) Framework: When Benz Meets Marconi
复制标题
支持 5G 的协作智能汽车 (5GenCIV) 框架：当奔驰遇上马可尼
DOI：
10.1109/mis.2017.53
复制
发表时间：
2017-05-01
影响因子：
6.4
通讯作者：
Yang, Yang
中科院分区：
文献类型：
--
作者：
Cheng, Xiang;Chen, Chen;Yang, Yang
作为当今最受欢迎的社交媒体平台之一，Twitter为人们提供了一种有效的沟通和互动方式。通过这些互动，用户的影响力逐渐显现，并改变了人们的观点。虽然前人的研究将人际影响作为信息传播过程中激活他人的概率来研究，但他们忽略了一个重要的事实，即信息传播是影响的结果，而用户之间的动态交互产生了影响。在这篇文章中，作者提出了一种新的时间影响力模型，通过探索在交流过程中如何产生影响力来学习用户对特定主题的意见行为。实验表明，与其他不同影响假设下的影响模型相比，他们的模型在预测用户的未来观点时表现得更好，尤其是对于观点多样性较高的用户。
As one of the most popular social media platforms today, Twitter provides people with an effective way to communicate and interact with each other. Through these interactions, influence among users gradually emerges and changes people's opinions. Although previous work has studied interpersonal influence as the probability of activating others during information diffusion, they ignore an important fact that information diffusion is the result of influence, while dynamic interactions among users produce influence. In this article, the authors propose a novel temporal influence model to learn users' opinion behaviors regarding a specific topic by exploring how influence emerges during communications. The experiments show that their model performs better than other influence models with different influence assumptions when predicting users' future opinions, especially for the users with high opinion diversity.