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首页> 外文会议>International Workshop on Data Quality and Trust in Big Data >SETTRUST: Social Exchange Theory Based Context-Aware Trust Prediction in Online Social Networks
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SETTRUST: Social Exchange Theory Based Context-Aware Trust Prediction in Online Social Networks

机译：SetTrust：基于社交交换理论的在线社交网络中的背景感知信任预测

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摘要

Trust is context-dependent. In real-world scenarios, people trust each other only in certain contexts. However, this concept has not been seriously taken into account in most of the existing trust prediction approaches in Online Social Networks (OSNs). In addition, very few attempts have been made on trust prediction based on social psychology theories. For decades, social psychology theories have attempted to explain people's behaviors in social networks; hence, employing such theories for trust prediction in OSNs will enhance accuracy. In this paper, we apply a well-known psychology theory, called Social Exchange Theory (SET), to evaluate the potential trust relation between users in OSNs. Based on SET, one person starts a relationship with another person, if and only if the costs of that relationship are less than its benefits. To evaluate potential trust relations in OSNs based on SET, we first propose some factors to capture the costs and benefits of a relationship. Then, based on these factors, we propose a trust metric called Trust Degree; at that point, we propose a trust prediction method, based on Matrix Factorization and apply the context of trust in a mathematical model. Finally, we conduct experiments on two real-world datasets to demonstrate the superior performance of our approach over the state-of-the-art approaches.
机译：信任是相关的上下文相关的。在真实世界的情景中，人们只在某些情况下相互信任。但是，在线社交网络中的大多数现有的信任预测方法（OSNS）中，这一概念并未严重考虑。此外，基于社会心理学理论的信任预测，非常少量尝试。几十年来，社会心理学理论试图在社交网络中解释人们的行为;因此，在OSNS中使用这些理论是为了获得奥斯诺斯的信任预测，将提高准确性。在本文中，我们应用了一个众所周知的心理学理论，称为社会交流理论（集），以评估奥斯人用户之间的潜在信任关系。基于套装，一个人与另一个人的关系，如果并且只有那种关系的成本低于其福利。为了评估基于集合的OSNS中的潜在信任关系，我们首先提出了一些因素来捕获关系的成本和益处。然后，根据这些因素，我们提出了一个称为信任程度的信任度量;此时，我们提出了一种基于矩阵分解的信任预测方法，并在数学模型中应用信任的背景。最后，我们对两个现实世界数据集进行实验，以展示我们对最先进的方法的方法的卓越表现。

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