定位你是谁：将地理位置与文本匹配以实现用户身份链接
Locate Who You Are: Matching Geo-location to Text for User Identity Linkage
摘要：
如今，鼓励用户同时在多个在线社交网络上激活。锚点链接预测旨在揭示跨网络的同一用户的不同帐户之间的对应关系，已被视为用户分析、营销、网络安全和推荐的基本问题。现有的方法主要通过以对称的方式利用用户的简档、内容或结构特征来解决预测问题。然而，在在线服务的鼓励下，用户也会在网络上发布不对称的信息，如地理位置和文本。它导致了一个新的挑战，在调整用户与网络上的信息不对称。而不是在以前的作品中应用的相似性评估，我们正式的地理位置和文本之间的相关性，并提出了一种新的锚链接预测框架，跨网络匹配用户。此外，我们的模型可以通过引入外部数据来缓解标签稀缺问题。在真实数据集上的实验结果表明，我们的方法优于现有的方法，并取得了最先进的结果。
英文摘要：
Nowadays, users are encouraged to activate across multiple online social networks simultaneously. Anchor link prediction, which aims to reveal the correspondence among different accounts of the same user across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel anchor link prediction framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external data. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
