Aggregated enterprise architecture wisdom
Following my previous posts on Netflix, I have been reading a detailed analysis in Ed Finn’s book, What Algorithms Want (2017).Finn’s answer to my question Does Big Data Drive Netflix Content? is no, at least not directly. Although Netflix had used dat…
Let us suppose we can divide the world into those who trust service companies to treat their customers fairly, and those who assume that service companies will be looking to exploit any customer weakness or lapse of attention.For example, some loyal cu…
This is one of a series of posts looking at the four key dimensions of data and information that must be addressed in a data strategy – reach, richness, agility and assurance.In my previous post, I looked at Reach, which is about the range of data sou…
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…
My thin clean-shaven friend @futureidentity is reassured by messages that appear to be misdirected.Have we reached peak hipster? I’ve just been sent an advertisement for a beard brush – right-handed model.— Robin Wilton (@futureidentity) April 1, 2016 …
Modern day marketing and advertising has created a paradox: customers want to be recognized and rewarded for their loyalty, but they also want their privacy to be respected and their data used ethically. We firmly believe that balancing this privacy-pe…
Why The Right Web Analytics Platform Is (Even More) Business Critical When it comes to understanding customers on their digital touchpoints most businesses Forrester speaks to have extended their practice way beyond simple browser analytics. Their inte…
I attended AdExchanger’s semi-annual Programmatic I/O conference last week to absorb the latest in industry talk and get a pulse check on the health and wellness of the programmatic ecosystem. This show was illuminating in that I found signs of both en…
The Case For Customer Empathy… Hubspot’s INBOUND 2017 slogan emphasized “Empathy.” My expectation: it would make the case to the 21,000 people in attendance that customer empathy is critical to their engagement efforts. I was for it — it’s something my research focuses on, and is something we in the CX and UX field try […]
Well, the privacy hits keep coming: another breach, more than a hundred million people affected, untold losses for another company and its customers. Next week, September 14-15 in Washington DC, Forrester is gathering experts in cybersecurity, privacy, customer experience, regulatory compliance, identity management, personalization, and a range of other related topics to bring clarity to […]
Digital retail theater is becoming reality for consumers as retailers rush to find ways of attracting and retaining store customers. What is digital retail theater? It’s the combination of original retail theater – entertainment, novelty, and engagement – with digital technologies and services to create the next level of in-store experience. My colleague, Brendan Miller […]
@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.
“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)