I am working on a request prediction problem in which I have to predict which object will be requested in the near future and how many times. This is like a basic internet traffic request pattern on a proxy cache. What I am doing is categorizing the new object in categories like "rare event", "moderatly requested", "frequently requested" etc. For each of the group, I have modelled the requested pattern by different log-normal interarrival distributions. This grouping is done based on few features like, type of object, age of object,etc.
Now I am trying to predict the next occurrence using their respective distribution.
I am using Gradient Boosting trees for the classification purpose.
I want to know, how can I update my model if my classification is going wrong. As I will have the exact information about the frequency of the object request only after some requests have occurred.
Is there a better way to model this problem ?
My other concern is, the object's popularity can change with time. How do deal with that?