I have a data set of users, and a list of pages each users liked.
My goal is to derive
k classes of users.
The first thing that comes to mind are bag-of-words models
The way I see it, there are 2 approaches:
I was thinking of applying LDA and get a posterior probability of classes for each user.
LDA is usually applied to topic classification of documents.
In the topic case, a word can be repeated several times in a document.
And in my case, a page can be liked only once by a user.
Another option is to apply pLSA, which is ok since I know the list of possible pages in advance