The goal of this project is to make a simple article recommendation engine using a natural language processing technique called word2vec. word2vec is an algorithm for constructing vector representations of words, also known as word embeddings. In particular, we're going to use a "database" from Stanford's GloVe project trained on a dump of Wikipedia.
Around the recommendation engine, I'm going to build a web server that displays a list of BBC articles. Clicking on one of those articles takes you to an article page that shows the text of the article as well as a list of five recommended articles.
Run server.py in local or on AWS will launch the server for the recommender engine.
Data (Glove database, BBC articles) is not included due to size issue.