60 FUTURESHOCK Vionlabs
We sat down with the team at Vionlabs, a company using AI in remarkable ways to get the right content to the right viewer at the right time
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lmost all the services we talk to now are looking at the content recommendation space, trying to understand which parts of it
are strategically important and which parts of it they need to outsource,” says Patrick Danckwardt, head of global business development at Swedish content discovery company Vionlabs. “Most of them have tried the pure metadata approach and have found that it didn’t really move the needle. It doesn’t mean that the algorithms are bad, it just means that the metadata they’ve been using hasn’t been strong enough. When we talk to them, they say: ‘We’ve already tried that. It didn’t work.’ But once we can show them how we are really going in depth with data, that’s when we spark their interest.” Stockholm-based Vionlabs is looking at the algorithms used for video recommendations in audaciously new ways. We are all familiar with film viewing recommendations, or suggestions for a product in a targeted ad that seem ridiculously misattuned: “When these digital WE’RE TEACHING THE COMPUTER HOW PEOPLE RESPOND TO IMAGERY AND SOUND
platforms seem know so much about me, how can they get my tastes so completely wrong?” Much of the problem boils down to good old GIGO and to what Vionlabs sees as an overly simplistic way of analysing content data and customer sentiment. GETTING RECOMMENDATIONS RIGHT The most basic method of content recommendation is metadata-based recommendation, which is the one most prone to the foibles of human error. This relies on comparisons made between the metadata that has been tagged to each film. Some metadata might have been carefully and thoughtfully constructed. It may have even been built with machine learning, cataloguing faces and elements in the film itself. Or it may have been typed in by a low-paid data jockey, who has 1000 films to catalogue and a few lines of ad copy as a guide. Or maybe there’s no metadata at all.
POP THE BUBBLE By truly understanding the pulse of the viewer, it is possible to make different recommendations based on the time of day and their emotional state, rather than relying on keywords only
The metadata content recommendation approach might note that you’ve enjoyed a film that features a car chase, and will then conclude you want more films with car chases. You now have a string of recommendations which include Terminator 2, The Fast and The Furious, 2 Fast 2 Furious and Smokey and the Bandit. If the content on the platform has been blessed with a deep and complex set of metadata, the results in this kind of recommendation can be perfectly adequate. Other times, the connections need some detective work before they become clear (“Ah, the reason I was recommended Steel Magnolias is because my favourite movie, The Deer Hunter, has steel workers in it).
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