A new weighting method in network-based recommendation

In this paper, we propose a influence-based approach to investigate network-based recommendation systems. Different from the previous mass diffusion approach, we give a new expression of initial resource distribution and take into account the influence of resources associated with the receiver nodes. According to ranking score and two measures about the degree of personalization, we demonstrate that our method can outperform the previous methods greatly. It’s found that there exists an optimal initial resource distribution that leads to the best algorithmic accuracy and personalization strength. The optimal initial resource distribution indicates that we should increase the initial resource located on popular objects, rather than decrease them.