  The recommendation methods based on network diffusion have been shown to
perform well in both recommendation accuracy and diversity. Nowdays, numerous
extensions have been made to further improve the performance of such methods.
However, to what extent can items be predicted by diffusion-based algorithms
still lack of understanding. Here, we mainly propose a method to quantify the
predictability of diffusion-based algorithms. Accordingly, we conduct
experiments on Movielens and Netflix data sets. The results show that the
higher recommendation accuracy based on diffusion algorithms can still be
achieved by optimizing the way of resource allocation on a density network. On
a sparse network, the possibility of improving accuracy is relatively low due
to the fact that the current accuracy of diffusion-based methods is very close
its predictability. In this case, we find that the predictability can be
improved significantly by multi-steps diffusion, especially for users with less
historical information. In contrast to common belief, there are plausible
circumstances where the higher predictability of diffusion-based methods do not
correspond to those users with more historical recording. Thus, we proposed the
diffusion coverage and item average degree to explain this phenomenon. In
addition, we demonstrate the recommendation accuracy in real online system is
overestimated by random partition used in the literature, suggesting the
recommendation in real online system may be a harder task.
