A computational approach towards food-wine recommendations

Food-wine pairing is an essential study in the culinary world and requires extensive research of the underlying food and wine pairing principles. To understand the principles of pairing, we must understand the characteristics of food, and wine, the interaction between them, and certain classic food-wine pairing norms that are religiously adhered to. To take this understanding to a broader class of masses, there is a need for a recommender system that considers all the trivial and non-trivial details of food-wine pairing and the preferences of individual users to provide the perfect pairing. To apply these pairing principles in the context of recommender systems, we require abstract features such as flavor, aroma, major ingredients, type of meat, and many others. To achieve this, our recommender system relies on text mining techniques and sentiment analysis at its core to extract the relevant information from the text in the dataset. Such extensive feature engineering has not been undertaken before in the context of generating food and wine recommendations. We have created a recommender system that em-bodies the age-old essence of wine paring, considering not only the food wine characteristics but also the user's preference of pairing (congruent or contrast).