ThankBeer: A Beer Recommendation Engine

Don’t thank me, thank beer

Abstract

We discuss a beer recommendation engine that predicts whether a user has had a given beer as well as the rating the user will assign that beer based on the beers the user has had and the assigned ratings. k-means clustering is used to group similar users for both prediction problems. This framework may be valuable to bars or breweries trying to learn the preferences of their demographic, to consumers wondering what beer to order next, or to beer judges trying to objectively assess quality despite subjective preferences.

Publication
Paper for CS229 at Stanford University, Fall Quarter 2012.
Bob Wilson
Bob Wilson
Decision Scientist

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