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 fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform 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, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.

用户身份链接是识别不同社交网络（SN）上同一用户身份的任务。先前的工作通过估计来自不同SN的身份之间的成对相似性，预测身份对的标签或基于相似性评分选择最相关的身份对来解决此问题。但是，这些方法大多数都无法利用先前匹配的身份的结果，这可能有助于后续匹配步骤中的后续链接。为了解决这个问题，我们将用户身份链接转换为序列决策问题，并提出了一种强化学习模型，以从全局角度优化链接策略。我们的方法充分利用了社交网络的结构和历史匹配的身份，同时探讨了处理匹配对后续决策的长期影响。我们在现实世界的数据集上进行了广泛的实验，结果表明我们的方法优于最新方法。