Could you build a recommender system with the frequency of purchase as the value? Is it possible to derive frequency of purchase?

You could build an item-based model with user_name, beer_id, frequency_of_purchase (the total count of their purchases). You could also use association rules to determine the probability of someone buying a beer, if they've bought other beers,

beer1, beer2, beer3 --> beer6.

Alternatively, you could create a binary matrix that contains all of the users and all of the possible beers with a 0/1 for each of the possible beers, depending on whether a user has purchased a beer or not. There is a fair amount of literature on this on the web, including a decent package in R (arules).

Could you build a recommender system with the frequency of purchase as the value? Is it possible to derive frequency of purchase?

You could build an item-based model with user_name, beer_id, frequency_of_purchase (the total count of their purchases). You would take the matrix of users/beers/frequency, call it Matrix A, and the matrix of similarity between beers in Matrix B, and multiply the two matrices to get the resulting recommendation in matrix C. You could also use association rules to determine the probability of someone buying a beer, if they've bought a set of other beers,

beer1, beer2, beer3 --> beer6.

With association rules, you could create a binary matrix that contains all of the users and all of the possible beers with a 0/1 for each of the possible beers, depending on whether a user has purchased a beer or not. There is a fair amount of literature on this on the web, including a decent package in R (arules).