From Practice to Theory in Learning from Massive Data

This talk will discuss examples of how Amazon applies machine learning to large-scale data, and open research questions inspired by these applications. One important question is how to distinguish between users that can be influenced, versus those who are merely likely to respond. Another question is how to measure and maximize the long-term benefit of movie and other recommendations. A third question, is how to share data while provably protecting the privacy of users. Note: Information in the talk is already public, and opinions expressed will be strictly personal.