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The back-end of the Drink This application is built on a recommender system. It is designed as a collaborative filtering algorithm to capture similarities in 'likeness' of a product among users. Think Netflix, but instead of movies, cocktails are the products in this application. In contrast to collaborative filtering, product-based (or content-based) filtering tries to figure out a particular user's favorite aspects of a product (or products) and recommend based on the attributes of the product(s).
Python is generally the go-to language to abstract the vast libraries of formulas that can be utilized in the recommender system. The gems used to access the euclidean_distances method within python are numpy, pandas, sklearn, metrics, and pairwise. Euclidean Distance is the distance between common cocktails among users of similar taste.