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This paper introduces the Beer2Vec model that allows the most popularalcoholic beverage in the world to be encoded into vectors enabling flavorfulrecommendations. We present our algorithm using a unique dataset focused on theanalysis of craft beers. We thoroughly explain how we encode the flavors andhow useful, from an empirical point of view, the beer vectors are to generatemeaningful recommendations. We also present three different ways to useBeer2Vec in a real-world environment to enlighten the pool of craft beerconsumers. Finally, we make our model and functionalities available toeverybody through a web application.

Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations