Sentence examples for content recommendation algorithm from inspiring English sources

Animation companies trying to find new audiences on YouTube fear they may struggle to attract viewers up against these other channels, which capitalise on YouTube's content recommendation algorithm.

Only time will tell if it was stupid or brilliant, but there's still the matter of "why?" Well, in pitching The Slide to publishers, the SimpleReach founders would be asked to explain how their content recommendation algorithm worked, and, in doing so, they'd talk about how social was a key component to the algorithm's secret sauce.

Those that profit will leverage video analytics and advanced content recommendation algorithms to deliver personalized viewing experiences across all connected devices.

Figure 3 shows the recommendation accuracy with transparent fusion comparison results of the proposed fusion mechanism of cooperative embedded filtering and crowd content recommendation (FCECR) algorithm and recommendation algorithm based on ant colony (RAAC).

Even with news that is not clearly fake, researchers have shown that Facebook's content-recommendation algorithms tend to serve up stuff that reinforces people's prejudices.

Anywhere you see the words "for you" or "recommended," you'll find content our recommendation algorithms think you'll find particularly interesting, based on your browsing and purchasing history, as well as other information if you've chosen to share it with us (your industry, for example).

ThiSometicle othernally appeared on VICE Uscholars

The company's proprietary recommendation algorithm suggests content to a user based on his/her online viewing habits, in a similar way to competitor Outbrain.

The company's recommendation algorithm doesn't reliably segment content by appropriate age level, and the sheer volume of YouTube content makes it difficult for parents to monitor what their children watch.

That's because observers believe YouTube's recommendation algorithm promotes videos with extreme and misleading content, predicting that this content will keep people engaged.

A Wall Street Journal investigation earlier this year found that YouTube's recommendation algorithm tended to drive viewers toward extremist content by suggesting edgier versions of whatever they were watching a good way to hold their attention.