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Using Probabilistic Confidence Models for Trust Inference in Web-Based Social Networks

机译：在基于Web的社交网络中使用概率置信度模型进行信任推断

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摘要

In this article, we describe a new approach that gives an explicit probabilistic interpretation for social networks. In particular, we focus on the observation that many existing Web-based trust-inference algorithms conflate the notions of "trust" and "confidence," and treat the amalgamation of the two concepts to compute the trust value associated with a social relationship. Unfortunately, the result of such an algorithm that merges trust and confidence is not a trust value, but rather a new variable in the inference process. Thus, it is hard to evaluate the outputs of such an algorithm in the context of trust inference. This article first describes a formal probabilistic network model for social networks that allows us to address that issue. Then we describe SUNNY, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and our confidence in the trust estimate and use the two values in order to compute an estimate of trust based on only those information sources with the highest confidence estimates. We present an experimental evaluation of SUNNY. In our experiments, SUNNY produced more accurate trust estimates than the well-known trust inference algorithm T_(IDAL)T_(RUST), demonstrating its effectiveness. Finally, we discuss the implications these results will have on systems designed for personalizing content and making recommendations.
机译：在本文中，我们描述了一种新方法，该方法为社交网络提供了明确的概率解释。特别是，我们关注于以下观察结果：许多现有的基于Web的信任推理算法将“信任”和“信任”的概念混合在一起，并处理两个概念的融合以计算与社会关系相关联的信任值。不幸的是，这种融合信任和置信度的算法的结果不是信任值，而是推理过程中的新变量。因此，很难在信任推理的情况下评估这种算法的输出。本文首先介绍了用于社交网络的正式概率网络模型，该模型使我们能够解决该问题。然后，我们描述SUNNY，这是一种新的信任推理算法，该算法使用概率抽样分别估计信任信息和我们对信任估计的信心，并使用这两个值来仅基于那些具有最高信心估计的信息源来计算信任的估计。我们提出SUNNY的实验评估。在我们的实验中，SUNNY产生了比著名的信任推断算法T_（IDAL）T_（RUST）更准确的信任估计，证明了其有效性。最后，我们讨论这些结果对设计用于个性化内容和提出建议的系统的影响。

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