1. A method for real-time estimation of user-specific click through rates (CTR) for ads, comprising using a computer to perform process actions for: receiving one or more sets of historical session data derived from one or more groups of users, said historical session data comprising time-stamped click and impression data for a plurality of ads in combination with user-specific features, said features including any of pageviews, URLs and keywords; processing the historical session data to eliminate any data corresponding to users identified as bots to generate a set of bot-free session data; processing the bot-free session data to generate a user behavior profile (UBP) for each user in the bot-free session data; processing the bot-free session data to eliminate statistically insignificant features by performing a statistical hypothesis test that retains only features having a number of associated clicks exceeding a specified minimum click level, and from the retained features, eliminating any features having a statistical association of positive or negative click events that is below a minimum statistical threshold, thereby creating a reduced feature set from the bot-free session data; generating a model of click-through behavior for each ad in the bot-free data for each UBP; for new user session data received in real-time, computing a score for each feature/ad pair based on the bot-free data and the reduced feature set for each new user based on the model of click-through behavior for each ad; estimating a CTR for each ad from the computed score for each ad; and serving one or more ads in real-time to each new user based on the estimated CTR for each ad for each new user.