  User identity linkage is a task of recognizing the identities of the same
user across different social networks (SN). Previous works tackle this problem
via estimating the pairwise similarity between identities from different SN,
predicting the label of identity pairs or selecting the most relevant identity
pair based on the similarity scores. However, most of these methods ignore the
results of previously matched identities, which could contribute to the linkage
in following matching steps. To address this problem, we convert user identity
linkage into a sequence decision problem and propose a reinforcement learning
model to optimize the linkage strategy from the global perspective. Our method
makes full use of both the social network structure and the history matched
identities, and explores the long-term influence of current matching on
subsequent decisions. We conduct experiments on different types of datasets,
the results show that our method achieves better performance than other
state-of-the-art methods.
