Case-based reasoning
Web-Based Reputation Management Systems: Problems and Suggested Solutions
Electronic Commerce Research
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
Representing Context for Multiagent Trust Modeling
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A survey of trust and reputation systems for online service provision
Decision Support Systems
Trust Modeling with Context Representation and Generalized Identities
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Toward a multidisciplinary model of context to support context-aware computing
Human-Computer Interaction
Modeling trust using transactional, numerical units
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
A survey of attack and defense techniques for reputation systems
ACM Computing Surveys (CSUR)
P2P reputation management: Probabilistic estimation vs. social networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Management in peer-to-peer systems
Bootstrapping trust evaluations through stereotypes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Modeling Decentralized Reputation-Based Trust for Initial Transactions in Digital Environments
ACM Transactions on Internet Technology (TOIT)
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The majority of existing trust and reputation models consider two types of knowledge in estimating the trustworthiness of a trustee in an interaction: personal direct experiences and recommendations from third parties. However, previous direct and recommended evidence is not available for new users. In addition, a new user joins the system with a neutral reputation value in most systems and must participate in interactions with others in order to raise its reputation score. Users usually tend to interact with high reputable ones; therefore, the chance of new-comers being selected for interaction is generally rare. As a result, it is hard for a new user to raise his or her reputation score. Furthermore, shortlived users preclude the others from gaining the necessary experiences to make an accurate evaluation. Even long-lived users might leave the system and rejoin with a new identity to lose their bad reputation and start with a neutral score. Hence, effective initialization mechanism is needed to avoid such problems in trust and reputation systems. We propose to use contextual information for bootstrapping the reputation value. We use the Maximum Likelihood Estimation method for trust initialization of probabilistic trust models. We show its implementation and effectiveness for a particular model called 'Beta reputation model' through simulations.