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.
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View Code? Open in Web Editor NEWRecommends 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.