时间二分网络中的趋势预测：Movielens，Netflix和Digg的案例
Trend prediction in temporal bipartite networks: the case of Movielens, Netflix, and Digg
摘要：
用户购买或收集某种物品的在线系统可以有效地表示为时间二分网络，其中节点和链接都随时间增加。我们使用这种表示来预测哪些项目可能在不久的将来变得流行。各种预测方法在三个不同的数据集上进行评估，这些数据集来自流行的在线服务（Movielens，Netflix和Digg）。我们表明，预测性能可以进一步提高，如果用户的社交网络是已知的，在这个网络中的个人用户的中心是用来衡量他们的行动。
英文摘要：
Online systems where users purchase or collect items of some kind can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
