Deep Learning for Recommender System I read about Recursive Neural Networks that they can convert Documents to distributed word representation. 

In the context of new article recommendation, I am thinking to use this model to convert all news articles to vectors and then recommend to a  particular user, news articles similar to ones he browsed. 

In vector space this will boil down to finding 'similar' vectors to a given vector(user's news read). 

How likely is that this model will work well in practice? Any comments and/or suggestions?
There are many ways to calculate similarity between articles. I have not seen anybody doing a vector conversion and running comparisons. However, there is a text mining strategy called "Term-Frequency / Inverse-Document-Frequency" which is a clever way to find unique words and phrases in documents. You can run this on multiple documents, and compare the extracted keywords to match them for recommendations.

Check out my ebook for more details: https://lizrush.gitbooks.io/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html

If you want to leverage this technique on web documents (like a blog), there is a free service to do so: https://algorithmia.com/recommends