主动学习在通信网络推荐系统中的应用

稀疏网络中大量潜在链接的存在对于链接预测问题是一个很大的挑战。在链接预测任务中引入主动学习，挖掘网络中大量未连接节点对中的潜在信息，从未标记样本中挑选出系统最不确定的样本交由用户判别。获得标记后的样本将给系统较高的信息增益。在通信网络数据集Nodobo中的实验结果表明，使用主动学习之后，该方法为通信用户预测潜在联系人的准确率得到显著的提高。

Application of active learning to recommender system in communication network

The existence of potential links in sparse networks becomes a big challenge for link prediction. The paper introduced active learning into the link prediction task in order to mine the potential information of a large number of unconnected node pairs in networks. The most uncertain ones of the unlabeled examples to the system were selected and then labeled by the users. These examples would give the system a higher information gain. The experimental results in a real communication network dataset Nodobo show that the proposed method using active learning improves the accuracy of predicting potential contacts for communication users.