arrow_back BDAS, the Berkeley Data Analytics Stack
What would you recommend?
Submitted by Anand (@anandk) on Friday, 11 April 2014
Section: Workshops Technical level: Intermediate
This workshop will provide the audience with a quick overview of recommendation systems & how to build one from scratch. We shall build user-user collaborative filter (CF) based recommendation engines as well as item-item CF recsys. The audience will get a flavour of the range of statistical & mathematical computations that go into a recsys.
Both beginners & mildly experienced folks would have a good set of skills to take away from this session. We will together de-mystify all that one imagines about recommendation systems.
We will cover what user-user CFs are as well as item-item CFs. We will then design and build an implementation (in R) of each & apply them to standard data sets. We will go into Mahout's pre-defined algorithms & how to use them (on a single node Hadoop setup).
Anand has been dabbling in systems software & application software development for the past 12 years. For the past few years his interest in data analytics has taken a lion's share of his energy. While not in front of the computer, he writes, travels, shoots (pictures), cooks (a wandering chef) and does a lot more he doesn't confess to.