Last year, Netflix outsourced one of its most difficult technical problem to the public: building a recommendation system for its movie rental service that works 10 percent better than the one it had. A year later, no one has solved the problem, even with a $1 million purse on the line. (For an interview with Jim Bennett, the vice president of recommendation systems at Netflix see “The $1 million Netflix Challenge”.) However, a team of engineers from AT&T has eked out an 8.43 percent improvement, better than any other team and good enough to take home the $50,000 “progress prize.”
The team’s approach was to combine more than 100 recommendation techniques into one mega system that looks for numerous different patterns in the Netflix data–essentially, its customers’ likes and dislikes. The Wall Street Journal has an interview with Bennett about the progress here.
Here’s how a Twitter engineer says it will break in the coming weeks
One insider says the company’s current staffing isn’t able to sustain the platform.
Technology that lets us “speak” to our dead relatives has arrived. Are we ready?
Digital clones of the people we love could forever change how we grieve.
How to befriend a crow
I watched a bunch of crows on TikTok and now I'm trying to connect with some local birds.
Starlink signals can be reverse-engineered to work like GPS—whether SpaceX likes it or not
Elon said no thanks to using his mega-constellation for navigation. Researchers went ahead anyway.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.