Addressing time bias in bipartite graph ranking for important node identification

For online service platforms such as Netflix, it is important to propose a list of high quality movies to their users. This type of problem can be regarded as a ranking problem in a bipartite network. This is a well-known problem, that can be solved by a ranking algorithm. However, many classical ranking algorithms share a common drawback: they tend to rank higher older movies rather than newer ones, though some new movies may be of higher quality. In the study, we develop a ranking method using a rebalance approach to decrease the time bias of the rankings in bipartite graphs. We then conduct experiments on three real datasets with ground truth benchmark. The results show that our proposed method not only reduces the time bias of the ranking scores, but also improves the prediction accuracy by at least 20%, and up to 80%.