For a side project I am trying to determine if users in a dataset are either "binge" users or not. I have a dataset of user log data, where each row is a user event.
Right now my algorithm is somewhat hackneyed but gets the job done. First, I calculate the time between events. I then label those time deltas as either "small" (0) or "large" (1), e.g. if a time delta is less than 10 minutes, it is "small", otherwise "large".
Then, I take the average of the labels for each user. So, binge activity for a user is on a scale of zero to one.
How can this be improved? What are other things to think about or look into the data to improve this rating?
EDIT: People can go greater than a week without using the service before coming back. So, utilizing an average of time deltas, etc. isn't as useful as it would be normally.