AD-Link:一种用户身份链接的自适应方法

Xin Mu, Wei Xie, R. Lee, Feida Zhu, Ee-Peng Lim
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引用次数: 5

摘要

用户身份链接(User identity linkage, UIL)是指将同一用户在不同网络社交平台上的账户进行链接。最先进的ui方法通常使用从概要文件属性、内容和关系派生的用户帐户特征来执行帐户匹配。然而,它们是静态的,不能很好地适应快速变化的在线社交数据,因为:(a)用户产生的新内容和活动;以及(b)向用户介绍的新平台功能。特别是,ui方法中使用的功能的重要性可能会随着时间的推移而改变,并且可能会引入新的重要用户功能。在本文中,我们提出了AD-Link,这是一种新的ui方法,它(i)学习并分配用于用户身份链接的用户特征的权重,(ii)处理新用户生成数据引入的新用户特征。我们在三个流行的在线社交平台(即Twitter、Facebook和Foursquare)的真实数据集上评估了AD-Link。结果表明,AD-Link优于最先进的UIL方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AD-Link: An Adaptive Approach for User Identity Linkage
User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account's features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a new UIL method which (i) learns and assigns weights to the user features used for user identity linkage and (ii) handles new user features introduced by new user-generated data. We evaluated AD-Link on real-world datasets from three popular online social platforms, namely, Twitter, Facebook and Foursquare. The results show that AD-Link outperforms the state-of-the-art UIL methods.
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