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Recommends articles to the user based upon an evaluation of similarity between the content of the article previously consumed by the user and the content of a corpus of 70,000 articles stored in a local NoSQL database. To extract topics from the corpus, three topic modeling techniques- LSA (Latent Semantic Analysis), LDA (Latent Dirichlet Analysis), and NMF (Non-negative Matrix Factorization) are applied and compared. These topics are then used to build the recommendation engine.