谁会分享我的照片？在线社交网络中内容扩散路径的预测
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
摘要：
内容流行度预测已经被广泛研究，因为它对Facebook，Instagram，Twitter和Pinterest等社交媒体网站的用户和主机的重要性和兴趣。然而，现有的工作主要集中在使用单个度量（例如喜欢或分享的总数）对流行度进行建模。在这项工作中，我们提出了Diffusion-LSTM，这是一种基于记忆的深度递归网络，可以学习递归地预测图像通过社交网络的整个扩散路径。通过结合用户的社会特征和图像特征，并编码的扩散路径，迄今为止所采取的一个显式的存储单元，我们的模型预测的扩散路径的图像更准确地相比，替代基线，要么只编码图像或社会特征，或缺乏记忆。通过将单个用户映射到用户原型，我们的模型可以推广到训练过程中没有看到的新用户。最后，我们证明了我们的模型的能力，生成的扩散树，并显示生成的树非常类似地面实况树。
英文摘要：
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model's capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
