跨社交网络的同一用户识别算法

User alignment across social networks

针对跨社交网络的同一用户识别问题,提出了一种综合用户兴趣、写作风格和档案属性的识别方法。通过在这3种不同的特征维度下分别判定用户关系,然后综合判定结果 ,提高同一用户识别准确性。其中,用户兴趣分为静态兴趣和动态兴趣,静态兴趣采用TextRank算法从用户背景信息中提取,动态兴趣则利用主题模型从用户发表的文本内容中挖掘出随时间变化的兴趣点。对于用户写作风格则通过One-Class SVM算法进行识别,最后利用信息熵赋权法比较用户档案属性相似度。实验结果表明,与传统机器学习算法相比,所提算法精确率、召回率均有所提升。

For the problem of identifying the same user across social networks,a recognition method that integrates user interests,writing style and profile attributes is proposed.By determining user relationships under these three different feature dimensions separately,and then synthesizing the results,the same user identification accuracy is improved.Among them,user interest is divided into static interest and dynamic interest,static interest is extracted from user background information by TextRank algorithm,while dynamic interest is mined from user published text content by using topic model to find out interest points that change over time.For user writing style,it is identified by One-Class SVM algorithm,and finally,the information entropy empowerment method is used to compare the similarity of user profile attributes.The experimental results show that the proposed algorithm has improved accuracy and recall rate compared with traditional machine learning algorithms.