Deep Learning for Recommender System
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