Screencast: Building a Recommendation Engine
In this video introduction to recommendation engines, Dr. Tuck Ngun breaks down the processes and data needed to algorithmically predict what your customers want and when.
Data-savvy online retailers and streaming content companies implement recommendation engines to support a customer experience that encourages better engagement and reduces churn. When executed properly, the financial impact of these systems is nothing to scoff at — Netflix reportedly values its system at $1 billion per year.
This screencast is presented by Tuck Ngun, data scientist and author of our introductory guide on building recommendation engines. In it, he explores key business uses of these valuable tools and demystifies some of the common machine learning algorithms that power them.