Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations

TL;DR

Beer2Vec is a novel model that encodes craft beer reviews into vectors, enabling personalized flavor-based beer recommendations and practical applications through a web platform.

Contribution

The paper introduces Beer2Vec, a new vector encoding method for beers based on reviews, facilitating flavor-aware recommendations and real-world deployment.

Findings

Beer2Vec effectively encodes flavor profiles from reviews.

The model improves personalized beer recommendations.

A web application demonstrates practical use cases.

Abstract

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.