Justified Assessments of Service Provider Reputation

服务提供商声誉合理评估（JASPR）旨在通过提供丰富的个性化服务声誉评估以及这些评估背后的原理来改进发现，选择和使用服务的方式。它特别针对中小型企业（SME），通过减少客户对广泛的市场历史或不透明的在线评论的依赖，使其更好地接触大客户。虽然大公司可以依靠品牌影响力吸引客户使用其服务，但中小企业很难获得市场准入，特别是那些没有多少历史可供客户借鉴的新企业，这可能意味着中小企业即使提供直接符合客户需求的服务也会被忽视。在客户方面，该项目将允许更智能的服务采购，基于反映供应商实际表现的丰富声誉评估，减少品牌和肤浅评论的偏见。更普遍地说，在任何基于服务的系统中，准确的声誉评估对于选择替代供应商至关重要。现有的方法通常基于被提供服务的客户端的直接体验和第三方推荐的组合来评估信誉，其中信誉被表示为数值分数或概率估计。它们不允许有机会询问一项评估，以查明为什么作出某一特定评估，从而查明这种评估是否适合于新的服务选择要求，而且它们排除了关于提供商以前行动背景的大量信息，而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，或者提供者可能改变了他们的组织关系。这些限制在涉及较新和较成熟的服务提供商的市场中特别重要。新的供应商往往在市场上处于不利地位，因为单一的负面评论可能会不成比例地损害他们的声誉，客户无法准确评估与新供应商相关联的风险。为了使更丰富的声誉评估，考虑到过去的服务提供的背景下，这种情况下必须建模和记录，并可以被描述为provenance.In建议的项目，我们将使用出处记录作为一个信息源，在此基础上，一个更细致入微的声誉机制可以基于。我们将定义支持算法和软件基础设施，以捕获、分析并向客户呈现这些丰富的声誉信息。

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.