2007 Interactive Measurement Optimization 
AWS Big Data
FEBRUARY 1, 2024
The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. Apart from generating regulatory reports, these teams require visibility into the health of the reporting systems.
The Unofficial Google Data Science Blog
APRIL 23, 2024
the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.
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Occam's Razor
APRIL 8, 2013
Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. Measure and decide what to do.
The Unofficial Google Data Science Blog
OCTOBER 7, 2015
by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.
Edwin Chen
MARCH 13, 2011
This is a summary of Bell and Koren’s 2007 Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights paper. We’ll see how using an optimization method to derive weights (as opposed to deriving weights via a similarity function) overcomes these two limitations.
Occam's Razor
OCTOBER 15, 2012
Note: The IAB (Interactive Advertising Bureau) and Google both benefit in their own way from the use of digital platforms. I spend 70% of my time in the US and for those discussions I'm primary looking at speed (connection above), mobile penetration (yes, 2007 was the year of mobile!), Most of the outcomes may still be offline.
The Unofficial Google Data Science Blog
APRIL 19, 2021
For these ML systems, calibration simplifies interaction. The numerical value of the signal became decoupled from the event it was measuring even as the ordinal value remained unchanged. isn’t good enough: it optimizes the calibration term, but pays the price in sharpness. And users may start receiving a lot more spam!
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