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Explaining reputation assessments

机译：解释声誉评估

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摘要

Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no accompanying explanation of the rationale behind the assessment. Such explanations would allow users or clients to make a richer assessment of providers, and tailor selection according to their preferences and current context. In this paper, we propose an approach to explain the rationale behind assessments from quantitative reputation models, by generating arguments that are combined to form explanations. Our approach adapts, extends and combines existing approaches for explaining decisions made using multi-attribute decision models in the context of reputation. We present example argument templates, and describe how to select their parameters using explanation algorithms. Our proposal was evaluated by means of a user study, which followed an existing protocol. Our results give evidence that although explanations present a subset of the information of trust scores, they are sufficient to equally evaluate providers recommended based on their trust score. Moreover, when explanation arguments reveal implicit model information, they are less persuasive than scores.
机译：声誉对于使人类或软件代理能够在替代提供商中选择的人来说至关重要。尽管存在几种有效的声誉评估方法，但它们通常将声誉扩展到数值代表中，但没有附带评估背后理由的解释。此类解释将允许用户或客户对提供者进行更丰富的评估，并根据其偏好和当前上下文来定制选择。在本文中，我们提出了一种通过产生组合以形成解释的参数来解释评估的理由。我们的方法适应，扩展并结合了现有方法来解释在声誉的背景下使用多属性决策模型进行的决策。我们提供示例参数模板，并介绍如何使用解释算法选择其参数。我们的提案是通过用户学习评估的，该提案遵循现有协议。我们的结果提供了证据，虽然解释呈现了信托分数信息的子集，但它们足以根据其信量得分建议的同样评估提供商。此外，当解释参数揭示隐式模型信息时，它们比分数不那么有说服力。

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