Using data from Beer Advocate, in the form of 1.5 million reviews, yhat shows how to build a recommendation system in R.
The goal for our system will be for a user to provide us with a beer that they know and love, and for us to recommend a new beer which they might like. To accomplish this, we’re going to use collaborative filtering. We’re going to compare 2 beers by ratings submitted by their common reviewers. Then, when one user writes similar reviews for two beers, we’ll then consider those two beers to be more similar to one another.
The simple recommender is at the end of the article. Select a beer you like, a type of beer you want to try, and you get a handful of beers you might like.
Obviously, the method isn’t exclusive to beer reviews, and this is just a start to a more advanced system that you can tailor to your own data. The good news is that the code to scrape data and recommend things is there for your disposal. [via @drewconway]
