Databases Reference
In-Depth Information
CHAPTER 8
Recommendation Engines:
Building a User-Facing Data
Product at Scale
Recommendation engines, also called recommendation systems, are
the quintessential data product and are a good starting point when
you're explaining to non-data scientists what you do or what data sci‐
ence really is. This is because many people have interacted with rec‐
ommendation systems when they've been suggested topics on Ama‐
zon.com or gotten recommended movies on Netflix. Beyond that,
however, they likely have not thought much about the engineering and
algorithms underlying those recommendations, nor the fact that their
behavior when they buy a topic or rate a movie is generating data that
then feeds back into the recommendation engine and leads to (hope‐
fully) improved recommendations for themselves and other people.
Aside from being a clear example of a product that literally uses data
as its fuel, another reason we call recommendation systems
“quintessential” is that building a solid recommendation system end-
to-end requires an understanding of linear algebra
and
an ability to
code; it also illustrates the challenges that Big Data poses when dealing
with a problem that makes intuitive sense, but that can get complicated
when implementing its solution at scale.
In this chapter,
Matt Gattis
walks us through what it took for him to
build a recommendation system for Hunch.com—including why he
made certain decisions, and how he thought about trade-offs between
