News Recommendations

[upbeat music] Welcome to Data Skeptic, a podcast exploring the methods, use cases, and consequences of recommender systems.

Welcome to another installment of Data Skeptic Recommender Systems.

Today on the show, we take on the topic of news recommendations.

Now, at a glance, this could seem like the type of problem where you could quickly adapt existing techniques to use.

Why can't you recommend news with the same methodology as you use for recommending movies? Well, there's a couple of reasons for that.

Obviously, movies can be more evergreen, whereas news stories are pretty timely, but also you get different feedback.

I'm happy to give thumbs up to a movie I like.

I'm not gonna thumbs up an article that's well-written about how innocent people died.

Those differences aside, the stakes are pretty high here.

If you recommend the wrong movie, at worst, you've wasted a person's time.

If you consistently recommend the wrong news stories, you could introduce bias or put a person inside of a bubble.

You know, the information highway has brought us a deluge of information, more than any one person could consume in a day.

So news recommendation systems are becoming more important than ever, and we'll be exploring that topic in today's interview.