When we typically talk about recommendation systems, we focus on specific novel algorithms and formulations for performing collaborative filtering.

However, building a system to recommend items to a user in a personalized way often involves many more components than just a collaborative filter; it requires a much broader ecosystem of functionality, tools, and development pipelines. This presentation will discuss an holistic approach to building recommendation systems including 1) how A/B testing works with machine learning to iterate toward better recommendations, 2) how to couple an information-retrieval based search stack with collaborative filtering to capture user intent in a personalized way, and 3) making recommendations more relevant and interpretable.

The slides for this talk are available on SlideShare.