I'm searching for a way to formulate my problem as a machine learning problem.
Suppose I have a history of user's locations, and I want to predict his next location, similar to how Google Now does it for Home/Work locations.
The problem is that I need to somehow encode user's current location, and the space of possible locations can be different. One way to approach this is to encode topK most frequent locations and use them as binary features, but then the model have to be rebuilt when topK locations change.
So I was thinking is there a better way to approach such problem?
UPDATE: Specifically, the set of locations is discrete (locations are public transport stations).