Trax: How Netflix knows what to recommend …

Consider this …

In March, Netflix shipped its 4 billionth DVD.

In the first quarter of 2013 alone, it streamed more than 4 billion hours.

The company estimates that 75 percent of viewer activity is driven by the company’s algorithmic recommendation.




Here’s some skinny on how Netflix works the numbers  …


According to Wired

Netflix has 800 engineers working behind the scenes on its algorithms.


Almost everything Netflix delivers – via DVD or steaming – is driven by a recommendation.

eBay says that 90 percent of what people buy there comes from search.

At Netflix, the opposite is true.

Recommendation is huge, and Netflix‘s search feature is what people do when they’re not able to show them what to watch.


By looking at the metadata, Netflix can find all kinds of similarities between shows. Were they created at roughly the same time? Do they tend to get the same ratings?


Testing has shown that the predicted ratings aren’t actually super-useful, while what you’re actually playing is.

We know that many of the ratings are aspirational rather than reflecting your daily activity.

For example, a lot of people tell us they often watch foreign movies or documentaries. But in practice, that doesn’t happen very much.


Netflix also looks at user behavior — browsing, playing, searching.

Viewing behavior is the most important data they have.

In a broad sense, most of Netflix ‘s algorithms are based on the assumption that similar viewing patterns represent similar user tastes.

They can use the behavior of similar users to infer others’ preferences.


We know what you played, searched for, or rated, as well as the time, date, and device.

We have been working for some time on introducing context into recommendations.

We have data that suggests there is different viewing behavior depending on the day of the week, the time of day, the device, and sometimes even the location.

This summer Netflix  will be unveiling a profile feature enabling family members to demarcate their preferences with individual queues. 

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