Justified Assessments of Service Provider Reputation

服务提供商声誉合理评估(JASPR)旨在改进发现、选择和使用服务的方式，为服务提供丰富、个性化的声誉评估，并提供这些评估背后的基本原理。它的特别目标是通过减少客户对广泛的市场历史或不透明的在线审查的依赖，使中小企业(SME)更好地接触大客户，这些在线审查没有考虑到个性化需求。虽然大公司可以依靠品牌影响力为客户带来服务，但中小企业很难获得市场准入，特别是较新的业务，客户几乎没有历史可供借鉴。这可能意味着，即使在提供与客户需求直接匹配的服务时，中小企业也会被忽视。在客户方面，该项目将允许更智能的服务采购，基于反映提供商实际表现的丰富声誉评估，较少偏向品牌和肤浅的审查。更广泛地说，在任何基于服务的系统中，准确的声誉评估对于在替代提供商之间进行选择至关重要。现有方法通常根据被提供服务的客户的直接体验和第三方推荐的组合来评估信誉，并且信誉被表示为数字分数或概率估计。它们不允许有机会审问评估以找出为什么做出特定的评估，因此它是否适合新的服务选择要求，并且它们排除了关于提供商先前行动的背景的大量信息，这些信息可能为客户选择服务提供商提供有用的信息。例如，过去的失败可能有减轻的情况，或者提供商可能已经改变了他们的组织从属关系。这些限制在涉及新的和更成熟的服务提供商的市场中尤其重要。新提供商在市场中往往处于劣势，因为一次负面评论可能会不成比例地损害他们的声誉，而且与现有提供商相比，客户无法准确评估与新提供商相关的风险。为了作出更丰富的声誉评估，并考虑到过去服务供应的背景，必须对这一背景进行建模和记录，并可以将其描述为供应的出处。在拟议的项目中，我们将使用出处记录作为信息来源，以便建立更细微的声誉机制。我们将定义支持算法和软件基础设施，以使这些丰富的声誉信息能够被捕获、分析并呈现给客户。

Justified Assessments of Service Provider Reputation (JASPR) aims to improve the way that services are discovered, selected and used by providing rich, personalised reputation assessments of services with the rationale behind those assessments. It is particularly targeted at giving small and medium-sized enterprises (SMEs) better exposure to large clients by reducing clients' reliance on extensive market histories or opaque online reviews that do not account for personalised needs. While large companies can rely on brand influence to bring clients to their services, it is difficult for SMEs to gain market access, especially newer businesses with little history for clients to draw on. This can mean that an SME is overlooked even when providing a service directly matching the client's needs. From the customer side, the project will allow more intelligent service procurement, based on rich reputation assessments reflecting the actual performance of providers, with less bias from branding and superficial reviews.More generally, in any service-based system, an accurate assessment of reputation is essential for selecting between alternative providers. Existing methods typically assess reputation on a combination of direct experiences by the client being provided with a service and third party recommendations, with the reputation expressed as a numerical score or probability estimate. They do not allow the opportunity to interrogate an assessment to find out why a particular assessment is made, and so whether it is appropriate to a new service selection requirement, and they exclude from consideration a wealth of information about the context of providers' previous actions that could give useful information to a customer in selecting a service provider. For example, there may be mitigating circumstances for past failures, or a provider may have changed their organisational affiliation. These limitations are of particular significance in marketplaces involving both newer and more established service providers. New providers are often disadvantaged in a marketplace since a single negative review can disproportionately harm their reputation, and customers are unable to accurately assess the risk associated with new providers compared to those that are established. To make richer reputation assessments that take into account the context of past service provisions, this context must be modelled and recorded, and can be described as the provenance of the provision.In the proposed project, we will use provenance records as a source of information on which a more nuanced reputation mechanism can be based. We will define the supporting algorithms and software infrastructure to allow this rich reputation information to be captured, analysed and presented to clients.