Stereotrust: A Group Based Personalized Trust Model

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Conference Proceeding Article

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Trust plays important roles in diverse decentralized environments, including our society at large. Computational trust models help to, for instance, guide users' judgements in online auction sites about other users; or determine quality of contributions in web 2.0 sites. Most of the existing trust models, however, require historical information about past behavior of a specific agent being evaluated - information that is not always available. In contrast, in real life interactions among users, in order to make the first guess about the trustworthiness of a stranger, we commonly use our "instinct" - essentially stereotypes developed from our past interactions with "similar" people. We propose StereoTrust, a computational trust model inspired by real life stereotypes. A user forms stereotypes using her previous transactions with other agents. A stereotype contains certain features of agents and an expected outcome of the transaction. These features can be taken from agents' profile information, or agents' observed behavior in the system. When facing a stranger, the stereotypes matching stranger's profile are aggregated to derive his expected trust. Additionally, when some information about stranger's previous transactions is available, StereoTrust uses it to refine the stereotype matching. According to our experiments, StereoTrust compares favorably with existing trust models that use different kind of information and more complete historical information. Moreover, because evaluation is done according to user's personal stereotypes, the system is completely distributed and the result obtained is personalized. StereoTrust can be used as a complimentary mechanism to provide the initial trust value for a stranger, especially when there is no trusted, common third parties.


group, reputation, stereotypes, trust model


Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics


ACM Conference on Information and Knowledge Management

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