As we know, the users in recommendation system are different, the new vs the regular, the active vs the inactive, etc. Is there any good method to fit this situation. I have some idea, but not smooth。 （1）construct multiple model for different people, waste much computation resource (2) use the obvious difference as feature (embedding look up). but this method may use a continual space to simulate the discrete space (different user may have totally different distribution) Does anybody have better idea?