a reputation-based trust model for peer-to-peer ecommerce communities [extended abstract]
Peer-to-Peer eCommerce communities are commonly perceived as an environment offering both opportunities and threats. One way to minimize threats in such an open community is to use community-based reputations, which can be computed, for example, through feedback about peers' transaction histories. Such reputation information can help estimating the trustworthiness and predicting the future behavior of peers. This paper presents a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system. There are two main features of our model. First, we argue that the trust models based solely on feedback from other peers in the community is inaccurate and ineffective. We introduce three basic trust parameters in computing trustworthiness of peers. In addition to feedback a peer receives through its transactions with other peers, we incorporate the total number of transactions a peer performs, and the credibility of the feedback sources into the model for evaluating the trustworthiness of peers. Second, we introduce two adaptive factors, the transaction context factor and the community context factor, to allow the metric to adapt to different domains and situations and to address common problems encountered in a variety of online communities. We also developed a concrete method to validate the proposed trust model and obtained initial results, showing the feasibility and benefit of our approach.
reputation trust model peer-to-peer ecommerce communities environments use computation examples feedback transaction histories informal help estimates trustworthiness predict future behavior paper coherence adapt systems feature credibility factor context metrication domain situated addressing online communities method electronic communities