Abstract
Linking user profiles belonging to the same people across multiple social networks underlines a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Most of existing approaches focus on pairwise user profile linkage between two platforms, which can not effectively piece up information from three or more social platforms. Different from the previous work, we investigate the user profile linkage across multiple social platforms by proposing an effective and efficient model called MCULK. The model contains two key components: 1) Generating a similarity graph based on user profile matching candidates. To speed up the generation, we employ the locality sensitive hashing (LSH) to block user profiles and only measure the similarity for the ones within the same bucket. 2) Linking user profiles based on similarity graph. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the superiority of our proposed model MCULK compared with the state-of-art methods.
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Acknowledgments
This work was supported by the Major Program of Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJA610002 and 19KJB520050, and the National Natural Science Foundation of China under Grant No. 61902270, a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Wang, M., Chen, W., Xu, J., Zhao, P., Zhao, L. (2020). User Profile Linkage Across Multiple Social Platforms. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_10
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DOI: https://doi.org/10.1007/978-3-030-62005-9_10
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