The subtle art of recommendation (algorithms)

Recommendation algorithms and their variations such as ranking are the most common way for machine learning to find its way into a product where it is not the main focus. In this talk we’ll dig into the subtleties of making recommendation algorithms a seamless and integral part of your UX (goal: it should completely fade into the background. The user should not be aware she’s interacting with any kind of machine learning, it should just feel right, perhaps smart or even a tad like cheating); how to solve the cold start problem (and having little training data in general); and how to effectively collect feedback data. I’ll be drawing from my experiences building Metabase, an open source analytics/BI tool, where we extensively use recommendations and ranking to keep users in a state of flow when exploring data; to help with discoverability; and as a way to gently teach analysis and visualization best practices; all on the way towards building an AI data scientist.