Application of active learning to recommender system in communication network

Abstract

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.