Learning to Trust on the Move
Neal Lathia · 2008
Abstract Computational trust has been developed as a novel means of coping with uncertainty within collaborative communities of interacting peers. The idea now of-fers enourmous potential for use in pervasive mobile environments; however, to date there is little agreement about what computational trust itself means, and what the limitations that emerge from its use are. In this work, we project the idea of com-putational trust into machine learning terms, showing that trust is a metaphor that helps system designers reason about and exploit the intended deployment scenario to achieve their goals. Viewing a trust model as a strategy to confront a learning problem thus allows us to explore the effect that constraints, such as mobility and user participation, will have on the quantity of information available to learn from; in this work, we demonstrate this idea with a set of experiments on the Reality Min-ing Dataset. The results highlight that the most successful trust models will be based on strong contextual information about the environment they are to be deployed in. 1