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Mikio Braun, Data Scientist and co-founder of Streamdrill, a start-up focusing on using stream mining technology to tackle real-time big data problems.   In this video he talks about “Real-time personalization and recommendation with stream mining”

OVERVIEW:

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.

Mikio discusses 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 across 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. (Via Berlin Buzzwords)

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Blog Publisher / Head of Data Science Search

Founder & Head of Data Science Search at Starbridge Partners, LLC.