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Canopy Brings Predictive Analytics and Data Privacy Together

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Canopy Brings Predictive Analytics and Data Privacy Together

Canopy wants to change the way content recommendations are made.

· Big Data Zone ·
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A New York-based startup, Canopy, recently announced it's looking to reinvent the way big data and AI algorithms learn about a person's interests and serve up relevant content. Built by former employees of Spotify, Canopy hopes to offer privacy to its users while still providing them with relevant, tailored content. During an interview with Techcrunch, Canopy's co-founder and CEO, Brian Whitman, said, "There hasn’t been a great solution to private discovery. We think the reason people haven’t been excited about privacy is that they haven’t seen the opportunities."

How does Canopy achieve anonymity while still learning about user behavior? For one, it doesn't require users to create an account or sign up in any way. In an attempt to build a "better kind of system," the builders of Canopy have erected a wall between users' devices and companies' servers. As most of the algorithms that organizations like Facebook, Netflix, and YouTube use to learn about user behavior happen on their company servers, this is a big step forward. 

This technology is only now surfacing thanks to advances in the hardware available on mobile devices. All the math behind Canopy's recommendations takes place on your phone, requiring quite a bit of computing capacity. Even just a few years ago, this service would have been impossible. 

According to Canopy's site, "Our app looks at things you like — songs you've played, places you've explored, articles you've read and loved — and makes a summary of your tastes... Every day, Canopy will send you a handful of new recommendations. We won't send you breaking news or current events, but rather stories and sounds to excite, inspire, and motivate you."

An interesting application in-and-of-itself, the team intends on selling Canopy as a SaaS product. According to CNET, this move has ramifications beyond the privacy of users, as it could help smaller organizations and startups compete with the giants behind modern recommendation algorithms. 

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Topics:
big data ,data privacy ,big data algorithms ,recommendation algorithms ,predictive analytics

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