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.
Built my first computer out of Lego bricks and learned to program soon after. Emergence, networks, modes of thought, limits of language and expression are what makes me smile (and stay up at night). The combination of lisp and machine learning put me on the path of always striving to make myself redundant if not outright obsolete. Currently working hard to become obsolete at Metabase where I am trying to build an artificial data scientist and imbue visualisations with understanding and context.