Bag of words and word2vec clarifications I have documents,  and I calculated the word vectors using word2vec for all the terms in my corpus.

Now I want to compute similarity between documents using the bag-of-words model.  After  creating the Bag-of-words ( consisting of  vector representations). 

how do I now compute similarity between a sequence of vectors? 

Do I simply just take the mean? and calculate the cosine similarity between the mean vectors? 

or is there a better approach for computing similarity between two bags?
BoW is a text representation like word2vec or doc 2vec. If you already have the word2vec vectors then you can take the mean for each document and calculate the similarity but it's not the direct approach. The direct approach is to calculate doc2vec which models the documents directly and with more detailed precision than word2vec.