Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to

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.

Many players in electronic markets have to cope with much higher amount of uncertainty as to quality and reliability of the products they buy and the information they obtain from other peers in the respective online business communities.

In this report we provide an overview of digital reputation management systems for virtual communities. We begin with a discussion on the how trust is created in the real world. We then highlight how the process of trust creation differs in the virtual world. Next, we talk about the different ways in which digital reputations are useful to virtual communities such as incentivizing cooperation and punishing ma-licious users, providing recommendations or as a form of distributed authentication. We then discuss how evolutionary biology and game theory have informed research in reputation systems. This is followed by a discussion of the various components of reputation such as context, the forms of evidence, first and second order repu-tation and architectural considerations including the role of distributed hash tables in decentralized reputation management systems. We conclude with a discussion of some reputation management systems such as complaints-based trust, EigenTrust, PeerTrust and ROCQ.

Collaborative systems available on the Web allow millions of users to share information through a growing collection of tools and platforms such as wikis, blogs, and shared forums. By their very nature, these systems contain resources and information with different quality levels. The open nature of these systems, however, makes it difficult for users to determine the quality of the available information and the reputation of its providers. Here, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user’s reputation. Finally, we validate these models using ground–truth Wikipedia data associated with vandals and administrators. When used as a classifier, the best model produces an area under the ROC curve of 0.98. Furthermore, we assess the reputation predictions generated by the models on other users, and show that all three models can be used efficiently for predicting user behavior in Wikipedia.