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Netflix offers details on its recommendation engine, says it guides 75 percent of viewership

Netflix offers details on its recommendation engine, says it guides 75 percent of viewership

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Neftlix has taken to its blog to post part one of an exploration into its recommendation engine. The writeup highlights just how many different things are "recommendations" on the website. It's not just the "Top 10" list that the algorithms need to make up, it's all of those different genres on the front page, which order to put the films in on each row, and what movies to recommend for their similarities to something else you've viewed.

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Netflix genre recommendation
Netflix genre recommendation

Netflix has taken to its blog to post part one of an exploration into its recommendation engine. The company offers little in terms of hard data and algorithms (those are promised for part two), but the writeup does highlight just how many different things are "recommendations" on the website. It's not just the "Top 10" list that the algorithms need to make up, it's all of those different genres on the front page, which order to put the films in on each row, and what movies are similar to other titles. It's not just some static list, either; Netflix says that it focuses on keeping results fresh and diversifying where and what order it recommends movies. If your entire household watches on the same account, movies tailored for different family members will pop up, providing options for everyone. And those quirky recommended genres like "Imaginative Time Travel Movies from the 1980s" look like they're here to stay — the company's data shows that there's a direct correlation between how likely a member is to stick with the service and how high up the page it places those specific genres.

The company says that its customers are so confident in the system at this point that 75 percent of all movies watched by members come from recommendations. If all of this talk of recommendation engines reminds you of the Netflix Prize competition that started in 2006, you'll be interested to hear that the million dollar-winning entry hasn't been implemented in any way. It turns out that it would've been too time consuming and costly to use the system on Netflix's massive scale, and by the time the prize was awarded the company was already shifting its focus away from DVDs and towards streaming, something that requires a different sort of recommendation tool altogether.