Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning.

通过跨领域信息融合框架（CasCIFF），我们针对信息级联预测提出了一种方法，该框架利用多跳邻域信息使用户嵌入更加鲁棒。在嵌入级联时，该框架有意地引入时间戳，赋予其捕捉信息扩散演变模式的能力。特别地，CasCIFF将用户分类和级联预测的任务无缝集成到一个统一的框架中，从而允许提取对所有任务都有用的公共特征，这是一种基于多任务学习原理的策略。

CasCIFF：一种专为社交网络级联预测量身定制的跨领域信息融合框架