A Hybrid Artificial Reputation Model

Abstract

Agent interaction in a community such as an online buyer-seller scenario is often risky and uncertain. An agent interacts with other agents where initially they know nothing about each other. Currently many reputation models are developed that help consumers select more reputable and reliable service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information, or they are built upon witness information, also known as word-of-mouth, that involves the reports provided by others. Neither the interaction trust nor the witness information models alone fully succeed in such uncertain interactions. This thesis research introduces the hybrid reputation model combining both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputed service providers, eventually lead to more gains by the consumer. Furthermore, the trust model developed is used in calculating trust values of service providers for the case study with a live website ecommerce