  Predicting the popularity of items in rating networks is an interesting but
challenging problem. This is especially so when an item has first appeared and
has received very few ratings. In this paper, we propose a novel approach to
predicting the future popularity of new items in rating networks, defining a
new bipartite clustering coefficient to predict the popularity of movies and
stories in the MovieLens and Digg networks respectively. We show that the
clustering behaviour of the first user who rates a new item gives insight into
the future popularity of that item. Our method predicts, with a success rate of
over 65% for the MovieLens network and over 50% for the Digg network, the
future popularity of an item. This is a major improvement on current results.
