  We propose a novel trust metric for social networks which is suitable for
application in recommender systems. It is personalised and dynamic and allows
to compute the indirect trust between two agents which are not neighbours based
on the direct trust between agents that are neighbours. In analogy to some
personalised versions of PageRank, this metric makes use of the concept of
feedback centrality and overcomes some of the limitations of other trust
metrics.In particular, it does not neglect cycles and other patterns
characterising social networks, as some other algorithms do. In order to apply
the metric to recommender systems, we propose a way to make trust dynamic over
time. We show by means of analytical approximations and computer simulations
that the metric has the desired properties. Finally, we carry out an empirical
validation on a dataset crawled from an Internet community and compare the
performance of a recommender system using our metric to one using collaborative
filtering.
