I am given a dataset where there are people profiles and the types of beer each person likes, given in a list. What is the best way to find relevant beers given a specific beer based on this data?
edit: I researched my question more and found that it is in the realm of collaborative filtering.
From my research, the examples that are currently given involve ratings or other metrics, but my dataset is very minimal. It includes the beers bought by a person. Therefore The only data we have is weather or not a person bought a specific beer.
The format is as such:
Person | Beers bought 1 A,B,C,R,G,S 2 A,F,U,I,T 3 B,C 4 J,R
From what I think, it appears to be an implicit item-based collaborative filter. My question, is, given one Beer say A, give the best recommendation of 3 beers.
My current attempt is given 1 beer, find all the users that bought it, then look at all the beers that they bought and find the 3 most common ones. I believe that that is too naive so what other methods are possible? I am thinking of having a weighting function but I am not sure how to go about it.