The Public Sector Network (PSN) invited me back this year to provide a keynote on our COVID-19 pandemic response for the Smart Communities Roadshow 2020 in Vancouver. I was fortunate to get to present the keynote in person at the 2019 roadshow last ye…
The amount of IT we have brought in the world is turning the human species into something ‘extended’. IT has behaviour and as such is an amplifier of our intentions. IT is us, it is inseparable from us. What culture does your organisation’s IT embody?
At #TalendConnect today frequent mention of #DataOps, although according to a post I found on the Talend blog from earlier this year, Talend prefers the term collaborative data management. Data Preparation … should be envisioned as a game-changing t…
The legendary R&B singer and songwriter Ben E. King got it so right in the Drifters 1960 recording of “This Magic Moment.” The song is timeless because it tells a classic tale of a first kiss. And also captures a moment in time in words and song th…
Here are a few thoughts from the SAP Leonardo Center launch in Singapore in early May. The good news: since we last reported on Leonardo, SAP is clearer about what Leonardo actually is, and has more mature messaging and interesting customer examples to…
At the packed NRF Big Show #NRF2018 in New York last week, many retailers looked at vendors touting new tech, including AI, AR/VR, and more, all in the name of improving customer experiences. But most of those same retailers know that, before leaping i…
We said this a year ago, but alas, we have to say it again. There’s a big insights-to-actions gap out there. Firms continue to invest in data, people, and technology, but in 2017, data and analytics pros reported basing fewer business decisions on dat…
Over the past year I’ve spoken formally and informally with hundreds of companies about their AI initiatives. The biggest AH-HA moment comes when these companies realize the difference between implementing traditional technology and applying anal…
Does it seem like the ability to find, hire and retain data scientists is a losing battle? Is spending $500K+ per year for a Data Scientist worth it? What is a data scientist anyway? Those a real questions and are the markers that how you are supportin…
Thomas Edison once said, “The value of an idea is in the using of it.” Today (many of) those “ideas” are data and the insights derived from them, and it remains true that their value is in how they are used. Simply put, data + use = value. Insights-driven companies use these data-derived insights in […]
@GuyLongworth, who teaches philosophy at Warwick, is puzzled by the Netflix recommendation algorithm.
Having seen both, I can only think that this must have to do with rhyme.
— Guy Longworth (@GuyLongworth) June 29, 2017
Philosopher Guy’s appeal to rhyme rather than reason seems to be based on the view that the two films have nothing else in common. But this is rather contradicted by the fact that he has actually seen both. Netflix has correctly surmised that people like Guy might possibly be interested in both films.
The first thing to understand about recommendation algorithms is that they are not solely (if at all) based on the intrinsic similarity of two products, but on what we might call relational similarity. If I tell you that people who like pizza also like ice-cream, that is primarily a statement about the “people who like”. You might try to explain this statement by observing that pizza and ice-cream both have a high fat content, but then so do lots of other foods.
And when someone has just eaten a pizza, it is perhaps more likely that they will go on to eat ice-cream next, rather than eating another pizza straightaway.
Would it be virtue signalling of me to reveal that I resisted the lure of the second pizza?
— Guy Longworth (@GuyLongworth) June 22, 2017
The second thing to understand is that recommendation algorithms work by trial and error. Netflix wants to know if Guy will accept its suggestion to re-watch Annie Hall, and this feedback will add to its knowledge of Guy as well as its knowledge of relational similarity between films.
Trial and error works better if you have a diverse range of trials. If you watch a couple of films in a particular genre, and then Netflix only ever shows you suggestions within that genre, it will never discover that you might be interested in a completely different genre as well. And you will never discover the full range of Netflix offerings, which could result in your abandoning Netflix altogether.
Diversity of suggestion adds to the richness of the experimental data that are generated. How many members of the “people like Guy” category respond positively to suggestion A, and how many to suggestion B? Todd Yellin, Netflix VP of Product, told journalists in March that “we are addicted to the methodology of A/B testing”.
What is genre anyway? In the past, genres (in book publishing, music, film, video games) were defined by the industry or by experts. In 2013, Netflix employed over 40 people hand-tagging TV shows and movies. But a data-driven approach allows genres to emerge organically from the patterns of consumption. Netflix (and Amazon and the rest) will be much more interested in data-defined genres than in industry-defined genres.
In her rant against the Netflix algorithm, @mehreenkasana makes two apparently contrary complaints. On the one hand, Netflix offers her content that is nothing like anything she has ever watched. She dismisses one suggestion with the words “I’ve never watched a show in a remotely similar vein.” On the other hand, she doesn’t see how Netflix can offer her challenging experiences. “Intensely curated experiences, whether you’re looking to explore movies or to meet people to date, remove one of the most critical aspects of a rich experience: risk, as in going out of your comfort zone.”
But as @larakiara explains, “personalization is key to ensuring users keep coming back. But there’s also the problem of over-personalization, so Netflix has to introduce variants.”
Thus we can see Netflix as an embodiment of at least three of @kevin2kelly’s Nine Laws of God.
- Control from the bottom up
- Maximize the fringes
- Honor your errors
“A trick will only work for a while, until everyone else is doing it.” (Remember Blockbuster.)
Mehreen Kasana, Netflix’s recommendation algorithm sucks (The Outline, 24 March 2017)
Kevin Kelly, Nine Laws of God. Chapter 24 of Out of Control (1994)
Lara O’Reilly, Netflix lifted the lid on how the algorithm that recommends you titles to watch actually works (Business Insider, 26 February 2016)
Janko Roettgers, Netflix Replacing Star Ratings With Thumbs Ups and Thumbs Downs (Variety, 16 March 2017)
Tom Vanderbilt, The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next (Wired, 7 August 2013)
@mrkwpalmer (TIBCO) invites us to take what he calls a Hyper-Darwinian approach to analytics. He observes that “many algorithms, once discovered, have a remarkably short shelf-life” and argues that one must be as good at “killing off weak or vanquished…