在基于声誉的信任形成中利用弱关联邻居的推荐

S. Hauke, M. Pyka, M. Borschbach, D. Heider
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引用次数: 1

摘要

个人之间的互动本质上依赖于信任,无论是发生在现实世界还是网络社区。在过去的几年中,已经提出了计算建模信任关系的建议,以帮助用户或用于多智能体系统中的建模目的。这些模型隐含地依赖于由参与实体(自主代理或互联网用户)建立的社会网络。然而,最先进的信任框架往往忽略了这些复杂网络的结构。在本文中,我们提出了一种新的方法,允许基于代理的信任框架利用来自所谓的弱联系的信息,否则这些信息将被忽略。提出了一种有效的基于协议度量的鲁棒投票方案,并通过仿真验证了该方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing Recommendations from Weakly Linked Neighbors in Reputation-Based Trust Formation
Interactions between individuals are inherently dependent upon trust, no matter if they occur in the real world or in cyber communities. Over the past years, proposals have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. These models rely implicitly on the social networks established by participating entities (be they autonomous agents or internet users). However, state-of-the-art trust frameworks often neglect the structure of those complex networks. In this paper, we present a new approach allowing agent-based trust frameworks to leverage information from so-called weak ties that would otherwise be neglected. An effective and robust voting scheme based on an agreement metric is presented and its benefit is shown through simulations.
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