Recommendation and personalization system usually use elaborate store and batch algorithms to periodically crunch user event data like views, ratings, or purchases to compute predictions. A downside of this approach is that recommendations do not reflect the current user behavior, leading to missed opportunities in making good recommendations, or out-dated recommendations, for example when the purchase has already been made. We discuss novel systems based on stream mining algorithms which accumulate statistics on user behavior in real-time in a streaming fashion, this way always reflecting the most recent user behavior. Comparing profiles accross different time-scales, one is also able to classify recent behavior which deviates from the long-term trend and might be particularly interesting. Such algorithms have applications in ad targeting, recommendation, retail, monitoring, some of which will be discussed in more detail.