  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.
