How Netflix Reverse Engineered Hollywood - Alexis C. Madrigal

#machine_learning et bien écrit (avec un plot twist à la fin évidemment)

Article de 2014.

Previously on seenthis : ▻https://seenthis.net/messages/213844

#algorithme #classification

comme l’a révélé une enquête de George Packer dans le New Yorker, le géant américain Amazon utilise sur son site un système de mise en avant baptisé –bel euphémisme– « Co-Op ». Ces fameux « cooperative advertising agreement » se traduisent en « recommandations », présentées comme le résultat de critères objectifs d’un algorithme, alors qu’elles sont en réalité de la #publicité financée par les éditeurs.

Cette #manipulation, non connue des clients, explique peut-être en partie la relative inefficacité des #suggestions d’#Amazon qui se traduisent moins qu’on ne le dit en actes d’achat.

Désormais, lorsqu’un média, une entreprise ou un artiste poste un message sur sa page Facebook, l’algorithme ne lui permet plus d’atteindre qu’un pourcentage infime de ses propres fans (autour de 5 à 7%). Facebook limite donc délibérément la portée des posts et conditionne, via son algorithme, la diffusion d’un contenu soit à son buzz initial (si le message est liké, partagé ou commenté, l’algorithme en élargit la publication), soit à l’achat d’espaces « sponsorisés ».

#curation

►http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679

#data_mining #text_mining

#tv_show

One day Facebook will adopt the same reductionist approach and classify us all, and then use these classifications to make educated suggestions “perhaps you (THX1138) would like to mate with Pamela (LUH3417) ?”.

Of course the NSA already has developed such a tool (called FLYPAPERPEELER) to identify terrorists who don’t know yet they are terrorists.

The vexing, remarkable conclusion is that when companies combine human intelligence and machine intelligence, some things happen that we cannot understand.

“Let me get philosophical for a minute. In a human world, life is made interesting by serendipity,” Yellin told me. “The more complexity you add to a machine world, you’re adding serendipity that you couldn’t imagine. Perry Mason is going to happen. These ghosts in the machine are always going to be a by-product of the complexity. And sometimes we call it a bug and sometimes we call it a feature.”

ohyes.

They capture dozens of different movie attributes. They even rate the moral status of characters. When these tags are combined with millions of users viewing habits, they become Netflix’s competitive advantage. The company’s main goal as a business is to gain and retain subscribers. And the genres that it displays to people are a key part of that strategy. “Members connect with these [genre] rows so well that we measure an increase in member retention by placing the most tailored rows higher on the page instead of lower,” the company revealed in a 2012 blog post. The better Netflix shows that it knows you, the likelier you are to stick around.

And now, they have a terrific advantage in their efforts to produce their own content: Netflix has created a database of American cinematic predilections. The data can’t tell them how to make a TV show, but it can tell them what they should be making. When they create a show like House of Cards, they aren’t guessing at what people want.

►https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679