中圖分類(lèi)號(hào)： TN01,；TP391 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211518 中文引用格式： 沈佳琪,，周?chē)?guó)民. 跨社交網(wǎng)絡(luò)的同一用戶識(shí)別算法[J].電子技術(shù)應(yīng)用，2022,，48(1)：109-114. 英文引用格式： Shen Jiaqi,，Zhou Guomin. User alignment across social networks[J]. Application of Electronic Technique，2022,，48(1)：109-114.
User alignment across social networks
Shen Jiaqi1,，Zhou Guomin2
1.College of Information Engineering，Zhejiang University of Technology,，Hangzhou 310023,，China； 2.Department of Computer and Information Security,，Zhejiang Police College,，Hangzhou 310053，China
Abstract： 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.
Key words : across social networks,；users identification,；user interest；writing style,；file attribute
