全视角特征结合众包的跨社交网络用户识别

随着互联网的普及和不断发展，用户通过多个社交网络进行社交活动，使用社交网络带来的丰富内容和服务.通过识别出不同社网上的同一用户，可以有助于进行用户推荐、行为分析、影响力最大化，因而显得尤为重要.已有方法主要基于用户的结构特征和属性特征来识别匹配用户，大多仅考虑局部结构，并且受已知匹配用户数量的限制.基于此，本文提出了一种基于全视角特征结合众包的跨社交网络用户识别方法（OCSA）.首先，利用众包来提高已知匹配用户的数量，接着，应用全视角特征评价用户的相似度，以提升用户匹配的准确性，最后，利用两阶段的迭代式匹配方法完成用户识别工作.实验结果表明该文提出的算法可显著提高用户识别的召回率和准确率，并解决了已知匹配用户数量不足时的识别问题.

Identifying Users Across Social Networks Based on Global View Features with Crowdsourcing

With the popularization and development of Internet, people prefer to take part in multiple social networks to enjoy different kinds of services. Consequently, a significant task is to identify users among the networks, which is helpful for user recommendation, behavior analysis and impact maximization. Most state-of-the-art works on this issue are mainly based on the user''s structure features and attributes features. They prefer to exploit user''s local features and limited by the number of the known matching users. In this paper, we proposes a method based on global view features to align users with crowdsourcing (OCSA). First, we use crowdsourcing to increase the number of known matching users on networks. Then, we use global view features to evaluate the similarity between users to improve the accuracy of user identification. Finally, we propose an iterative two-stage matching method to answer the user identification. The results of experiments show that our method has better performance on precision and recall, especially when the number of known matching users is insufficient.