If you are signed in to Google News, do a search or two and click on a few stories. Come back to the Google News front page. There should be a section added to the front page, "Recommended for X", with list of recommended stories.
Just like early experiments with personalized news at MSNBC and MSN Newsbot, only the "Recommended for X" section seems to change, not the entire front page.
So, how does it work? From the help page:
Google News can suggest news stories just for you. If you have Personalized Search enabled, you can sign in to your Google Account to get recommended news stories based on your past news selections.Personalization comes to Google News.
Google News can ... compare your tastes to the aggregate tastes of other groups of similar Google News users. Simply put, we recommend news stories to you that have been read by many other users who've also read similar stories as you in the past.
It is unclear exactly what technique Google News is using for their news recommendations. Their description implies they are using some kind of collaborative filtering or clustering-based technique.
However, when I used the news widget in Google Sidebar (part of Google Desktop), which also learns from the articles you read, it appeared that the recommendations were based on subject categories. So, read a technology article, get another technology article.
In my usage of the new Google News feature, I found the recommendations also seemed to be showing most popular articles based on topic. Clicking on three articles on Google brought up articles on space probes, digital music, WiFi, and intelligent design. A quick check of Google's most popular articles for tech and science turned up many of these articles. Clicking on a few more articles about Google did not change the recommendations noticeably.
This would suggest that the underlying recommendation engine either is using some combination of subjects and similar subjects (e.g. read an article on internet technology, get recommendations of popular articles in subjects similar to internet technology) or a clustering approach (build a large number of groups of different readers with various interests, match you to the most appropriate cluster based on your history, recommend things popular with other readers in the cluster).
That's okay, but the problem is that the recommendations will tilt toward the popular and away from the tail. You want the recommendations to be surprising, to enhance discovery, to help me find things I wouldn't have found on my own. Clustering or subject-based techniques won't get you there.
Very cool stuff. Now that Google is doing both personalized search and personalized news, I expect we'll see a lot more activity in this area.
See also reviews and thoughts from Gary Price, Philipp Lenssen, and Steve Rubel.
Update: If you are signed in and still cannot see the recommendations, make sure Search History is enabled.
Update: Nathan Weinberg posts a review. He says, "If Google's personalization engine works, it is either very subtle, or needs a lot of data." It's a great point. Changing immediately, obviously, and appropriately on any new data is important. Google seems to have missed that.
Update: Principal Scientist Krishna Bharat announces on the official Google Blog that Google News is now out of beta and talks about the new recommendation feature: "You'll receive recommended news stories based on the previous stories you've read ... All of this is done automatically using algorithms. For example, we might recommend news stories to you that many other users have read, especially when you and they have read similar stories in the past."