Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations
This provides a domain-specific tool for craft beer consumers to receive personalized recommendations.
The paper tackles the problem of generating beer recommendations by developing Beer2Vec, a model that encodes beer flavors into vectors from craft beer reviews, and demonstrates its usefulness through empirical evaluation and a web application.
This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations. We present our algorithm using a unique dataset focused on the analysis of craft beers. We thoroughly explain how we encode the flavors and how useful, from an empirical point of view, the beer vectors are to generate meaningful recommendations. We also present three different ways to use Beer2Vec in a real-world environment to enlighten the pool of craft beer consumers. Finally, we make our model and functionalities available to everybody through a web application.