Abstract
User influence analysis in social media has attracted tremendous interest from both the sociology and social data mining. It is becoming a hot topic recently. However, most approaches ignore the temporal characteristic that hidden behind the comments and articles of users. In this paper, we introduce a Tensor Factorization based on User Cluster (TFUC) model to predict the ranking of users’ influence in micro blogs. Initially, TFUC obtain an influential users cluster by neural network clustering algorithm. Then, TFUC choose influential users to construct tensor model. A time matrix restrain TFUC expect CP decomposition and ranked users by their influence score that obtained from predicted tensor at last. Our experimental results show that the MAP of TFUC is higher than existing influence models with 3.4% at least.
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Acknowledgement
This research project was supported by the National Natural Science Foundation of China (No. 61772135 and No. U1605251), the Open Project of Key Laboratory of Network Data Science & Technology of Chinese Academy of Sciences (No. CASNDST201606) and the Director’s Project Fund of Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education (No. 2017KF01).
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Liao, X., Zhang, L., Gui, L., Wong, KF., Chen, G. (2018). A Tensor Factorization Based User Influence Analysis Method with Clustering and Temporal Constraint. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_77
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DOI: https://doi.org/10.1007/978-3-319-73618-1_77
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