Abstract
Reputation systems have been extensively explored in various disciplines and application areas. A problem in this context is that the computation engines applied by most reputation systems available are designed from scratch and rarely consider well established concepts and achievements made by others. Thus, approved models and promising approaches may get lost in the shuffle. In this work, we aim to foster reuse in respect of trust and reputation systems by providing a hierarchical component taxonomy of computation engines which serves as a natural framework for the design of new reputation systems. In order to assist the design process we, furthermore, provide a component repository that contains design knowledge on both a conceptual and an implementation level. To evaluate our approach we conduct a descriptive scenario-based analysis which shows that it has an obvious utility from a practical point of view. Matching the identified components and the properties of trust introduced in literature, we finally show which properties of trust are widely covered by common models and which aspects have only rarely been considered so far.
Highlights
In the last decade, trust and reputation have been extensively explored in various disciplines and application areas
We aim to foster reuse in respect of trust and reputation systems by providing a hierarchical component taxonomy of computation engines which serves as a natural framework for the design of new reputation systems
We argue that the reusable components we identify in this work could extend current reputation models by the ability to gradually include the properties of trust described above
Summary
Trust and reputation have been extensively explored in various disciplines and application areas. Thereby, a wide range of metrics and computation methods for reputation-based trust has been proposed. While most common systems have been introduced in e-commerce, such as eBay’s reputation system [1] that allows to rate sellers and buyers, considerable research has been done in the context of peer-to-peer networks, mobile ad hoc networks, social networks or ensuring data accuracy, relevance and quality in several environments [2]. Computation methods applied range from simple arithmetic over statistical approaches up to graph-based models involving multiple factors such as context information, propagation or personal preferences. A general problem is that most of the newly introduced trust and reputation models use computation methods that are designed from scratch and rely on one novel idea which could lead to better solutions [3]. Approved models and promising approaches may get lost in the shuffle
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