Mix and match analytics: data, metadata, and machine learning for the win ZDNet

YouTube recommendations are a prominent example of applying advanced analytics on a massive scale to improve a service, the experience users get out of it, and the bottom line of the vendor behind it -- Google. Previously, we explored the rationale behind it and pondered as to how this type of analytics could be classified. It's time to pick up where we left off and explore how it works under the hood. Inspiration came from a hit moment for YouTube recommendations: one of those times when it succeeded in picking up the track that the person curating an ad-hoc, spur-of-the-moment playlist was about to play next. The wow effect induced by this successful prediction/recommendation of a rarity, triggered a spur-of-the-moment discussion which may serve to illuminate different aspects of analytics.