Impression based billing and bid based advertisement is an in demand feature for all monetizing applications. Just like Facebook advertisement model, in which the advertiser has to pay as much times as the ad is being viewed. The challenges of such a system involves intelligent, transparent and fair preference order for advertisements for the right people. The open nature of this systems brings competition into play and fair order in that case is maintained using relative time and preference swapping. The allocation for limited advertisement slots takes into account the user preference and learns over time what kind of ads the user is willing to see. We used clustering methodologies to predict and weight the preference of the user.