A reputation-based trust model for peer-to-peer eCommerce communities

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 to help evaluating the trustworthiness and predicting the future behavior of peers. This paper presents PeerTrust - 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 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. We argue that the trust models based solely on feedback from other peers in the community, is inaccurate and ineffective. Second, we introduce two adaptive trust factors, the transaction context factor and the community context factor, to allow the basic trust metric to incorporate different contexts (situations) and to address common problems encountered in a variety of online eCommerce communities. We present a concrete method to validate the proposed trust model and report the set of simulation-based experiments, showing the feasibility and benefit of the PeerTrust model.