  In this paper, we present a model of a trust-based recommendation system on a
social network. The idea of the model is that agents use their social network
to reach information and their trust relationships to filter it. We investigate
how the dynamics of trust among agents affect the performance of the system by
comparing it to a frequency-based recommendation system. Furthermore, we
identify the impact of network density, preference heterogeneity among agents,
and knowledge sparseness to be crucial factors for the performance of the
system. The system self-organises in a state with performance near to the
optimum; the performance on the global level is an emergent property of the
system, achieved without explicit coordination from the local interactions of
agents.
