  Recently, how to expand data transmission to reduce cell data and repeated
cell transmission has received more and more research attention. In mobile
social networks, content popularity prediction has always been an important
part of traffic offloading and expanding data dissemination. However, current
mainstream content popularity prediction methods only use the number of
downloads and shares or the distribution of user interests, which do not
consider important time and geographic location information in mobile social
networks, and all of data is from OSN which is not same as MSN. In this work,
we propose D2D Long Short-Term Memory (D2D-LSTM), a deep neural network based
on LSTM, which is designed to predict a complete D2D diffusion path. Our work
is the first attempt in the world to use real data of MSN to predict diffusion
path with deep neural networks which conforms to the D2D structure. Compared to
linear sequence networks, only learn users' social features without time
distribution or GPS distribution and files' content features, our model can
predict the propagation path more accurately (up to 85.858\%) and can reach
convergence faster (less than 100 steps) because of the neural network that
conforms to the D2D structure and combines user social features and files
features. Moreover, we can simulate generating a D2D propagation tree. After
experiment and comparison, it is found to be very similar to the ground-truth
trees. Finally, we define a user prototype refinement that can more accurately
describe the propagation sharing habits of a prototype user (including content
preferences, time preferences, and geographic location preferences), and
experimentally validate the predictions when the user prototype is added to
1000 classes, it is almost identical to the 50 categories.
