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Foursquare stores and processes everything from check-ins to screen views using a combination of home grown and open source tools. This talk covers an overview of our stack, highlighting specific examples of how, and why, it grew to what it is today and continues with the many ways that this infrastructure is employed.
One such example is our data-driven product development with the recently launched recommendations engine, named “Explore.” Explore recycles past check-in data into signals like venue similarity and time-sensitive popularity measures, resulting in intelligent recommendations building upon past user behavior as well as social and bookmarking features.
This talk takes a closer look at how Explore, and other features, emerged from our data analysis as well as the iterative process of monitoring and improvement that is critical for making such features a success.
Before joining foursquare, Justin worked at a hedge fund as a quantitative analyst, building custom portfolios for their asset management division and doing modeling and analysis for their risk team, specializing in high-frequency, derivatives, and commodities trading. Prior to that, he worked for Bear Stearns as a vice president in their fixed income analyst group, building applications and models to help value agency pass-thru securities and building loan-level pricing applications and models. Justin holds a BS in computer science with a minor in mathematics from the University of Rochester and has studied graduate-level math and computer science at Columbia University. He enjoys finding ways to take large sets of data and making interesting things happen with it.
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