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EVALUATION OF COLLECTING REVIEWS IN CENTRALIZED ONLINE REPUTATION SYSTEMS

机译:在集中式在线声誉系统中收集审查的评价

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Background: Centralized Online Reputation Systems (ORS) have been widely used by internet companies. They collect users' opinions on products, transactions and events as reputation information then aggregate and publish the information to the public. Aim: Studies of reputation systems evaluation to date have tended to focus on isolated systems or their aggregating algorithms only. This paper proposes an evaluation mechanism to measure different reputation systems in the same context. Method: Reputation systems naturally have differing interfaces, and track different aspects of user behavior, however, from information system perspective, they all share five underlying components: Input, Processing, Storage, Output and Feedback Loop. Therefore, reputation systems can be divided into these five components and measured by their properties respectively. Results: The paper concentrates on the evaluation of Input and develops a set of simple formulas to represent the cost of reputation information collection. This is then applied to three different sites and the resulting analysis shows the pros and cons of the differing approaches of each of these sites.
机译:背景:集中式在线声誉系统(ORS)已被互联网公司广泛使用。他们将用户对产品,事务和事件的意见作为声誉信息,然后汇总并将信息发布到公众。目的:迄今为止的声誉系统评估的研究已经倾向于专注于孤立的系统或其聚合算法。本文提出了一种在同一背景下测量不同声誉系统的评估机制。方法:声誉系统自然具有不同的接口,并跟踪用户行为的不同方面,但是,从信息系统的角度来看,它们都共享五个底层组件:输入,处理,存储,输出和反馈循环。因此,声誉系统可以分为这五个组件并分别通过其性质测量。结果:纸张专注于对输入的评估,开发一套简单的公式,以代表信誉信息收集的成本。然后将其应用于三个不同的部位,所得到的分析显示了这些位点中的每一个的不同方法的优缺点。

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