How Recommendation Algorithms Work—And Why They May Miss the Mark. ... Self-learning algorithms train to recognize patterns in the data so they, too, can predict what content a person might like.

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Indeed, 95% of the potential predictive accuracy that a machine learning algorithm might achieve is obtainable just from friends’ data. Average predictability from your circle of closest friends ...

For the subset of users who understand the impact of the algorithms enough to try to appease them, the answer, then, is yes—that makes one boring; but the “that” is not the users’ predictability but ...

Publicly, executives have said that the recommendations algorithm drives over 70 percent of content watched on YouTube, and that they’re getting better and better at it all the time.

This week, recommendation algorithms were front and center at the Supreme Court. The Court heard a pair of cases: Gonzalez V. Google and Twitter v. Taamneh, Tuesday and Wednesday, respectively ...

This, in turn, should pre-empt issues with developers trying to game the system by choosing popular tags or leaning on positive reviews, as they have with previous recommendation algorithms.

Sometimes those recommendations are obvious: "Because [your friend] watched this," or you might get served an entire row of action films near the top of the screen because that's your favorite genre.

Twitter announced on Friday that it's open-sourcing the code behind the recommendation algorithm the platform uses to select the contents of the users' For You timeline.