标题： RLINK：深度强化学习用于用户身份关联 显示英文标题

标题： RLINK: Deep Reinforcement Learning for User Identity Linkage

摘要： 用户身份关联是一项在不同的社交网络（SN）中识别相同用户身份的任务。先前的研究通过估计来自不同社交网络的身份之间的成对相似性，预测身份对的标签或根据相似性分数选择最相关身份对来解决这个问题。然而，大多数这些方法忽略了之前匹配身份的结果，这些结果可能有助于后续匹配步骤中的关联。为了解决这个问题，我们将用户身份关联转换为序列决策问题，并提出了一种强化学习模型从全局视角优化关联策略。我们的方法充分利用了社交网络结构和之前匹配的身份，并探索了当前匹配对后续决策的长期影响。我们在不同类型的数据集上进行了实验，结果表明我们的方法比其他最先进的方法表现更好。 显示英文摘要

摘要： 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.