Grapevine: A Wine Prediction Algorithm Using Multi-dimensional Clustering Methods

Wine has incredible diversity; there exist over 10,000 different varieties of wine grapes worldwide, and each can be processed in a hundred thousand unique ways. Sommeliers-- those who dedicate their lives to the art of wine tasting-- work to craft flavor profiles for the wines they are given to analyze, using their extensive experience to provide nuanced evaluations of countless bottles of wine every year. But the majority of people have neither the time nor the money to try a variety of wines and develop their palate. Typically, the only claim one can make about a given glass of wine is whether or not it was enjoyable, and without the ability to identify ones taste preferences in wine, it is incredibly difficult for one to discover new wine, and nearly impossible to find wine that directly matches their individual flavor profile. We hope to develop an algorithm to address both of these issues, becoming a personal sommelier for the user. Our algorithm takes a history of the wine a user has tasted as input, and returns a set of optimal wines for the user to try next, as well as a description of the flavor profile that inspired the recommendations. Thus, the algorithm could become an avenue for the user to confidently explore wine, and understand more quickly what they do and do not like in wine. Formally, we define our problem as an unsupervised learning problem.