Friday, November 24, 2006

My AOL launches news recommendations

Sam Sethi at TechCrunch UK reports that My AOL integrated "personalized content recommendations" into their beta feed reader.

There are two sections, "People Like Me Content" and "Recommended Content". The "People Like Me Content" help popup says:
As other people use My AOL, they occasionally click on stories that are similar to the items you have selected. Our system recognizes these similarities and provides additional content that might be of interest to you.
The "Recommended Content" help popup says:
These are personalized content recommendations. As you click on headlines within My AOL, we (well, our computers) “learn” what you like and suggest similar stories.
The difference is not apparent to me -- both say they use your reading history to find similar content -- but perhaps one group of recommendations are user-based and the other is content-based.

The service appears quite similar to Findory -- perhaps even inspired by Findory -- but the quality of the recommendations seems a bit off in my tests.

For example, I clicked on six articles on My AOL, a TechCrunch article about Google Blog Search and the five most recent articles from my weblog, Geeking with Greg, which happen to all be about Microsoft, Google, Yahoo, and Amazon. My "People Like Me Content" was:
  • RR of the Day: 1984 Alfa Romeo GTV6
  • Google's poo apparently doesn't smell
  • Big Brother Is Listening
  • Virtualization Disallowed For Vista Home
  • EPA to Regulate Nanoproducts Sold As Germ-Killing
  • Suspect Captured at Miami Herald
  • British Government Attacks Own Citizens
  • USA TODAY: Teacher's Space Goal Delayed 21 Years
Not good at all. However, the "Recommended Content" was quite a bit better, though very tightly focused on search:
  • Interesting Yahoo result in Google Search
  • My favorite blogger/blog of the moment...
  • Tracking a package through MSN Search
  • RSS mashup: Amazon, eBay, Yahoo! product results
  • Finding Search related Jobs
  • Community Powered Search
  • Become helps you search a little closer
  • Yahoo! vs. Google -- more or less peanut butter?
For comparison, here are the recommendations you get if you click the same articles on
  • Google's Kirkland Office
  • The Inefficiency of Feed Readers
  • Google AdSense Gift 2006: Digital Photo Frame
  • Search Engine Thanksgiving Logos 2006
  • Top Web Apps in Serbia
  • Canadian ISPs Launch Fight Against Child Porn
  • Yahoo & Peanut Butter Market Timing
  • AbbreviationZ : Acronyms & Abbreviations Search Engine
Certainly, My AOL's news recommendations are not bad for a first effort. I would expect them to improve in time as they gather more data and refine their algorithms.

I find it very interesting to see this new feature coming out of AOL. Other than Google Personalized Search, this effort from My AOL looks like the biggest use of recommendations and personalization from the search giants so far, bigger than the recommended stories in Google News and MSN Newsbot and the feed suggestions in Bloglines.

I wonder if we will soon see additional personalization and recommendation features launched by the search giants, not just in news and feeds, but also in podcasts, videos, search, and advertising.

Update: In the comments for this post, Jim Simmons (PM, Personalization, AOL) confirms that the difference between "Recommended content" and "People Like Me Content" is content-based vs. user-behavior-based recommendations.


Anonymous said...

I think they are exposing thier 2 different alogorithms inad hence they are saying it in the form of two different features

Anonymous said...


Thanks for taking time to check out the personalization features in myAOL.

To clarify, the Recommended Stories are derived from clickstream behavior within myAOL (subscribed feeds and recommendations). The system simply matches individuals with content related to their interests. “People Like Me” is a little different and is closer to a collaborative filtering service. It recommends stories that people with similar interests have also read.

It would be possible to combine both to create a single list of recommendations, but we felt it would be interesting to present them separately.


Jim Simmons
Product Manager, Personalization

Greg Linden said...

Hi, Jim. I am flattered to see you are reading my weblog. Thanks for the clarification!