word2vec did inspire earlier iterations of the model, but the key insight is that embeddings are learned jointly with all other model parameters. There is no separate source of embeddings. This way, embeddings are specialized for the the specific task.
In general what could be a separate source of embeddings? Also, how do these embeddings compare against traditional CF based latent factors?(I ask this in terms of a recommender metric and not complexity)