Databases Reference
In-Depth Information
would give you some insight: don't show ads in the first five minutes!
Now to study this more, you really would want to do some A/B testing
(see
Chapter 11
), but this initial model and feature selection would
help you prioritize the types of tests you might want to run.
It's also worth noting that features that have to do with the user's be‐
havior (user played 10 times this month) are qualitatively different
than features that have to do with your behavior (you showed 10 ads,
and you changed the dragon to be red instead of green). There's a
causation/correlation issue here. If there's a correlation of getting a
high number of points in the first month with returning to play next
month, does that mean if you just
give
users a high number of points
this month without them playing at all, they'll come back? No! It's not
the number of points that caused them to come back, it's that they're
really into playing the game (a confounding factor), which correlates
with both their coming back
and
their getting a high number of points.
You therefore would want to do feature selection with
all
variables, but
then focus on the ones you can do something about (e.g., show fewer
ads) conditional on user attributes.
David Huffaker: Google's Hybrid Approach to
Social Research
David's focus is on the effective marriages of both qualitative and
quantitative research, and of big and little data. Large amounts of big
quantitative data can be more effectively extracted if you take the time
to think on a small scale carefully first, and then leverage what you
learned on the small scale to the larger scale. And vice versa, you might
find patterns in the large dataset that you want to investigate by digging
in deeper by doing intensive usability studies with a handful of people,
to add more color to the phenomenon you are seeing, or verify inter‐
pretations by connecting your exploratory data analysis on the large
dataset with relevant academic literature.
David was one of Rachel's colleagues at Google. They had a successful
collaboration—starting with complementary skill sets, an explosion
of goodness ensued when they were put together to work on Google+
(Google's social layer) with a bunch of other people, especially software
engineers and computer scientists. David brings a social scientist per‐
spective to the analysis of social networks. He's strong in quantitative
methods for understanding and analyzing online social behavior. He
got a PhD in media, technology, and society from Northwestern
