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%.

对于诸如Netflix之类的在线服务平台，向用户推荐高质量电影列表非常重要。这种类型的问题可以视为双向网络中的排名问题。这是一个众所周知的问题，可以通过排名算法解决。但是，许多经典的排名算法都有一个共同的缺点：尽管某些新电影的质量可能更高，但它们倾向于对较高级别的老电影而不是较新的电影进行排名。在这项研究中，我们开发了一种使用重新平衡方法的排名方法，以减少二部图中排名的时间偏差。然后，我们对具有地面真实性基准的三个真实数据集进行实验。结果表明，我们提出的方法不仅减少了排名分数的时间偏差，而且将预测准确性提高了至少20％，最高可达80％。