Grapevine: A Wine Prediction Algorithm Using Multi-dimensional Clustering Methods
This is an incremental improvement for wine consumers seeking personalized recommendations.
The researchers tackled the problem of wine recommendation by developing an algorithm that uses multidimensional clustering on wine reviews to identify flavor palates and then optimizes recommendations based on user preferences and price-quality ratios within those clusters.
We present a method for a wine recommendation system that employs multidimensional clustering and unsupervised learning methods. Our algorithm first performs clustering on a large corpus of wine reviews. It then uses the resulting wine clusters as an approximation of the most common flavor palates, recommending a user a wine by optimizing over a price-quality ratio within clusters that they demonstrated a preference for.