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EEUPL: Towards effective and efficient user profile linkage across multiple social platforms
World Wide Web ( IF 3.7 ) Pub Date : 2021-06-12 , DOI: 10.1007/s11280-021-00882-7
Manman Wang , Weiqing Wang , Wei Chen , Lei Zhao

Linking user profiles belonging to the same people across multiple social networks underlines a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Most of existing approaches focus on pairwise user profile linkage between two platforms, which can not effectively piece up information from three or more social platforms. Different from the previous work, we investigate scalable user profile linkage across multiple social platforms by proposing an effective and efficient model called EEUPL, which can detect duplicate profiles within one platform that belong to same person and is implemented with Apache Spark for distributed execution. The model contains two key components: 1) To link cross-platform user profiles effectively, we propose an average-link strategy based clustering method. 2) To extend the model EEUPL to large-scale datasets, an Apache Spark based approach is developed. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the superiority of the model EEUPL compared with the state-of-art methods.



中文翻译:

EEUPL:跨多个社交平台实现有效和高效的用户配置文件链接

跨多个社交网络将属于同一个人的用户档案链接起来强调了广泛的应用,例如跨平台预测、跨平台推荐和广告。现有的方法大多侧重于两个平台之间的成对用户资料链接,无法有效地拼凑来自三个或更多社交平台的信息。与之前的工作不同,我们通过提出一种称为 EEUPL 的有效且高效的模型来研究跨多个社交平台的可扩展用户配置文件链接,该模型可以检测一个平台内属于同一个人并使用 Apache Spark 实现分布式执行的重复配置文件。该模型包含两个关键组件:1)为了有效地链接跨平台用户配置文件,我们提出了一种基于平均链接策略的聚类方法。2) 为了将模型 EEUPL 扩展到大规模数据集,开发了一种基于 Apache Spark 的方法。在两个真实世界的数据集上进行了大量实验,结果证明了模型 EEUPL 与最先进的方法相比的优越性。

更新日期:2021-06-13
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