Could you elaborate on "we learn high dimensional embeddings for each video in a fixed vocabulary and feed these embeddings into a feedforward neural network."

So, each video is mapped to fixed size vector of floats? A user's history is now a matrix of size [number of videos, embedding size]? What are the other parameters in this sentence "Importantly, the embeddings are learned jointly with all other model parameters through normal gradient descent back propagation updates."? And how do you concatenate all these into a "wide layer" when users would have histories of different length?