Trust models have become invaluable in dynamic scenarios, such as Internet applications, since they provide means for estimating trustworthiness of potential interaction counterparts. Currently, the majority of trust models require ratings to be expressed absolutely, that is as values from some predefined scale. However, literature shows that expressing ratings absolutely can be challenging for users and susceptible to their bias. But these issues can be tackled if instead of asking users to rate with absolute values, we ask them to express preferences between pairs of alternatives. Thus, in this paper we propose a trust model where pairwise comparisons are used as ratings and where trust is expressed as a strict partial order induced over agents. To maintain a sound ordering, the model uses a belief revision technique that prevents contradictions that may arise when adding new information. The technique uses mechanisms that reason quantitatively about the reliability of information allowing the model to time-discount ratings as well as withstand deceit. We evaluate the model in a series of experiments and compare the results against established trust models. The results show that the model quickly adapts to changes, gracefully handles deceitful, noisy and biased information, and generally achieves good accuracy.

信任模型在动态场景（例如Internet应用程序）中已变得无价，因为它们提供了估算潜在交互对应对象的信任度的方法。当前，大多数信任模型都要求绝对表达评级，即以某种预定义比例的值表示。但是，文献表明，表达评分绝对对用户具有挑战性，并且容易受到他们的偏见的影响。但是，如果不要求用户在成对的替代方案之间表达偏好，而不是要求用户对绝对值进行评分，则可以解决这些问题。因此，在本文中，我们提出了一种信任模型，其中将成对比较用作评分，并将信任表示为对代理诱导的严格偏序。为了保持声音秩序，该模型使用信念修正技术，可防止添加新信息时可能出现的矛盾。该技术使用的机制对信息的可靠性进行定量推理，从而使模型能够对时间打分以及抵制欺骗。我们通过一系列实验评估该模型，并将结果与​​已建立的信任模型进行比较。结果表明，该模型能够快速适应变化，并能很好地处理欺骗性，嘈杂和有偏见的信息，并且总体上具有良好的准确性。我们通过一系列实验评估该模型，并将结果与​​已建立的信任模型进行比较。结果表明，该模型能够快速适应变化，并能很好地处理欺骗性，嘈杂和有偏见的信息，并且总体上具有良好的准确性。我们通过一系列实验评估该模型，并将结果与​​已建立的信任模型进行比较。结果表明，该模型能够快速适应变化，并能很好地处理欺骗性，嘈杂和有偏见的信息，并且总体上具有良好的准确性。