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Timothy Alland, Brandon Butler, Phuc Nguyen, Aidan Lorenz


We built a beer recommender app that recommends beers to a user based on a list of beers that the user likes. The underlying model uses matrix factorization trained on a data set of ~1.5 million reviews with ~65,000 different beers and ~33,000 users.

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