Word2Vec - document similarity Lets say I have text data for different documents from 2005 - 2015. I want to compare the similarity between $t$ and $t-1$ documents. So I take the document at 2006 and compare it with the document at 2005, take the document at 2007 and compare with the document at 2006 … all the way to 2015, compared with 2014.

I have computed for each year a Word2Vec model independently of other year and obtained high dimensional arrays for each Word. So I have 10 Word2Vec models from 2005 - 2015.

Whats the best way for me to compare the documents similarity from here.

Previously I used TF-IDF where I was able to for each document have a large matrix with words in the rows and documents in the columns. Then I could combine he TermDocumentMatrix at $t$ and $t-1$ and compute the cosine similarity.

However Word2Vec gives much high dimensional arrays and I cannot think how to compare W2V models from $t$ to $t-1$.

Any help would be great!
you might want to look into 'approximate nearest neighbors' analysis, and particularly the annoy package from Spotify: 

https://github.com/spotify/annoy

It's not as precise as an exact comparison, but it may be close enough to get you what you need.