Netflix is a major player in the so-called "big data" game, as the success of programs like House of Cards demonstrates, but they don't envy larger companies like Google or Facebook for one simple reason.
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Transcript - So it's funny, big data has been kind of a cliché in Silicon Valley for the last few years: big data this, big data that. Big data is really one big mountain of garbage with little gems buried it in this tremendous trash heap, and you want to find those gems — you really want to find out what's going to make the experience better. So there are a lot of sophisticated machine learning algorithms that Netflix and other companies deploy to really figure out what are the gems that are going to make a better experience, and what's the rubbish that you want to separate out and push to the side? Once you find those gems, it doesn't make it a more alienated, machine experience — it actually makes it a more personal experience. It becomes much more about the individual member.
When I first got to Netflix we were looking at other companies that were doing personalization and leveraging the kinds of data they couldn't learn from. And one company that obviously wasn't competitive with Netflix was also doing some interesting things was Pandora, the music company. And Netflix is in Silicon Valley and they're up in Oakland, not too far away, and we're down in the South Bay. So we went up to - we had a meeting, a little powwow, this was many years ago, with Pandora. And they were really small then and Netflix was much smaller and we were just comparing notes. What was interesting about Pandora is Pandora had the Music Genome Project where they were tearing apart and deconstructing lots of music on all these different dimensions and trying to really understand the music. And I remember back in these days, and this was like ten years ago, they had their walls lined with CDs all over and they had a whole line of people in this cramped office with headphones on and they were listening to music with this big spreadsheet open and tagging everything about it.
At that's time at Netflix we were all about rating our titles on a one to five star system and we were very much using a lot of behavioral - a lot of algorithms around the behaviors of what users were doing and based on a lot of clustering techniques. We weren't really deconstructing the titles yet, we would get to that soon after — they weren't really deploying a lot of the collaborating filtering models that we were using on our algorithms. So we compared notes and we influenced each other and we only met a couple of times with them and were paying attention to what other companies were doing in terms of personalization. And based on these learnings, everyone kind of evolved across, it's not just Netflix, other companies that are trying to leverage big data to make it much easier to find something great to watch, great to listen to, great to read, great to buy, and figure out how to use, when to use human created data, a lot of metadata, a lot of deconstructing of what the material is people are watching or listening to or reading and so forth and how to use a lot of behavioral clustering data, what kinds of people are watching these kinds of shows and movies? What kinds of people are watching these or listening to these and so forth? Read Full Transcript Here: .
Read more at BigThink.com:
Follow Big Think here:
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Transcript - So it's funny, big data has been kind of a cliché in Silicon Valley for the last few years: big data this, big data that. Big data is really one big mountain of garbage with little gems buried it in this tremendous trash heap, and you want to find those gems — you really want to find out what's going to make the experience better. So there are a lot of sophisticated machine learning algorithms that Netflix and other companies deploy to really figure out what are the gems that are going to make a better experience, and what's the rubbish that you want to separate out and push to the side? Once you find those gems, it doesn't make it a more alienated, machine experience — it actually makes it a more personal experience. It becomes much more about the individual member.
When I first got to Netflix we were looking at other companies that were doing personalization and leveraging the kinds of data they couldn't learn from. And one company that obviously wasn't competitive with Netflix was also doing some interesting things was Pandora, the music company. And Netflix is in Silicon Valley and they're up in Oakland, not too far away, and we're down in the South Bay. So we went up to - we had a meeting, a little powwow, this was many years ago, with Pandora. And they were really small then and Netflix was much smaller and we were just comparing notes. What was interesting about Pandora is Pandora had the Music Genome Project where they were tearing apart and deconstructing lots of music on all these different dimensions and trying to really understand the music. And I remember back in these days, and this was like ten years ago, they had their walls lined with CDs all over and they had a whole line of people in this cramped office with headphones on and they were listening to music with this big spreadsheet open and tagging everything about it.
At that's time at Netflix we were all about rating our titles on a one to five star system and we were very much using a lot of behavioral - a lot of algorithms around the behaviors of what users were doing and based on a lot of clustering techniques. We weren't really deconstructing the titles yet, we would get to that soon after — they weren't really deploying a lot of the collaborating filtering models that we were using on our algorithms. So we compared notes and we influenced each other and we only met a couple of times with them and were paying attention to what other companies were doing in terms of personalization. And based on these learnings, everyone kind of evolved across, it's not just Netflix, other companies that are trying to leverage big data to make it much easier to find something great to watch, great to listen to, great to read, great to buy, and figure out how to use, when to use human created data, a lot of metadata, a lot of deconstructing of what the material is people are watching or listening to or reading and so forth and how to use a lot of behavioral clustering data, what kinds of people are watching these kinds of shows and movies? What kinds of people are watching these or listening to these and so forth? Read Full Transcript Here: .
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