SimON-Feedback: An Iterative Algorithm for Performance Tuning in Online Social Simulation

M. Vora, Wingyan Chung, Cagri Toraman, Yifan Huang
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引用次数: 1

Abstract

Simulation of human behaviour being an intrinsically difficult problem, no single algorithm or model can accurately simulate online social networks. One can obtain an optimal and reliable simulation only after combining several models focusing on diverse social aspects. Since all independent models focus on different social aspects, it is inherently difficult to combine and optimize their performance. Moreover blackbox nature of these predictive algorithm makes it difficult to integrate human-guided intelligence. Here we are presenting SimON-Feedback, an iterative ensemble algorithm to combine the prediction of several independent models into a significantly improved simulation of an online social network. To this end, we explore user posting and commenting behavior on Reddit, a large social networking platform comprised of many communities called as subreddits.
SimON-Feedback:一种用于在线社交模拟性能调整的迭代算法
人类行为的模拟本质上是一个困难的问题,没有单一的算法或模型可以准确地模拟在线社交网络。只有将关注不同社会方面的几个模型结合起来,才能得到最优可靠的仿真结果。由于所有独立的模型都关注不同的社会方面,因此很难组合和优化它们的性能。此外,这些预测算法的黑箱特性使得人工智能难以整合。在这里,我们提出了SimON-Feedback,这是一种迭代集成算法,将几个独立模型的预测结合到一个在线社交网络的显著改进模拟中。为此,我们研究了用户在Reddit上的发帖和评论行为,Reddit是一个由许多被称为subreddits的社区组成的大型社交网络平台。
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