Abstract
This paper proposes a retweeter predction model based on attention model and Tranformer encoding–Forwader Prediction Model based on User Preference (FPM-UP). Considering the impact of release time, content, and current external context information in the forwarding process, FPM-UP integrates user attribute embedding and context-user dependency into a temporal and text attention model for the prediction of the next forwarding user. Compared with the existing methods, FPM-UP significantly improves the prediction accuracy.
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Acknowledgement
This work was supported by the Open Funding Projects of the State Key Laboratory of Communication Content Cognition (No. 20K05 and No. A02107).
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Chunyan, T. et al. (2023). Context-User Dependent Model for Cascade Retweeter Prediction. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_61
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DOI: https://doi.org/10.1007/978-3-031-28124-2_61
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