Integrating Indicators of Trustworthiness into Reputation-Based Trust Models

AutorHauke, Sascha; Volk, Florian; Habib, Sheikh Mahbub; Mühlhäuser, Max
ArtConference Proceedings
AbstraktReputation-based trust models are essentially reinforcement learning mechanisms reliant on feedback. As such, they face a cold start problem when attempting to assess an unknown service partner. State-of-the-art models address this by incorporating dispositional knowledge, the derivation of which is not described regularly. We propose three mechanisms for integrating knowledge readily available in cyber-physical services (e.g., online ordering) to determine the trust disposition of consumers towards unknown services (and their providers). These reputation-building indicators of trustworthiness can serve as cues for trust-based decision making in eCommerce scenarios and drive the evolution of reputation-based trust models towards trust management systems.
KonferenzProceedings of the 6th International Conference on Trust Management (IFIPTM 2012)