  In this paper, based on a weighted object network, we propose a
recommendation algorithm, which is sensitive to the configuration of initial
resource distribution. Even under the simplest case with binary resource, the
current algorithm has remarkably higher accuracy than the widely applied global
ranking method and collaborative filtering. Furthermore, we introduce a free
parameter $\beta$ to regulate the initial configuration of resource. The
numerical results indicate that decreasing the initial resource located on
popular objects can further improve the algorithmic accuracy. More
significantly, we argue that a better algorithm should simultaneously have
higher accuracy and be more personal. According to a newly proposed measure
about the degree of personalization, we demonstrate that a degree-dependent
initial configuration can outperform the uniform case for both accuracy and
personalization strength.
