基于用户兴趣的跨网络用户身份识别算法

针对现有算法对用户兴趣在跨网络用户身份识别中作用的忽视以及时间复杂度高的问题，提出了基于用户兴趣的跨社交网络用户身份识别算法（UI-UI）。首先利用分块思想对用户节点进行初筛选，以提升算法效率、降低时间复杂度；其次，根据用户产生内容（UGC）和用户社交关系对用户兴趣进行建模，并计算兴趣相似度作为身份识别的依据；最后利用半监督学习的方法进行跨网络用户身份识别。通过在真实社交网络中进行实验，结果表明UI-UI算法能有效识别跨网络用户，且准确率和召回率稳定，运行时间显著减少。

User identification across social networks based on user interests

Aiming at the problem of ignoring the role of user interest in user identification across social networks and the high time complexity, this paper proposed a user identity algorithm based on user interest(UI-UI). Firstly, this algorithm filtered the user nodes by blocking to improve the efficiency of the algorithm and reduce the time complexity. Secondly, it modeled the user''s interest according to the UGC and user social relations, and used the similarity of user interest as the basis for user identification. Finally, it used the method of semi-supervised learning for user identification. Experiments on real social networks show that the UI-UI algorithm can effectively identify cross-network users, and both the accuracy and recall rate of the algorithm are stable, besides, the running time is significantly reduced.