期刊
IEEE ACCESS
卷 7, 期 -, 页码 47114-47123出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2909089
关键词
User identification; frequent pattern; cross-social network; information entropy
资金
- National Natural Science Foundation of China [61771185, 61772175, 61801171]
- Science and Technology Research Project of Henan Province [182102210044, 182102210285]
- Key Scientific Research Program of Henan Higher Education [18A510009]
- Postdoctoral Science Foundation of China [2018M632772]
The precision of the conventional user identification algorithm is not satisfactory because it ignores the role of user-generated data in identity matching. In this paper, we propose a frequent pattern mining-based cross-social network user identification algorithm that analyzes user-generated data in a personalized manner. We adopt the posterior probability-based information entropy weight allocation method that improves the precision rate and recall rate compared to the empirical weight allocation method. The extensive simulations are provided to demonstrate that the proposed algorithm can enhance the precision rate, recall rate, as well as the F-Measure (F1).
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