Saturday, March 01, 2008

People who read this article also read

I have an article in the March 2008 issue of IEEE Spectrum titled "People Who Read This Article Also Read...".

The article is on personalized news. An excerpt from the introduction:
So far, few newspaper sites look different from the pulp-and-ink papers that spawned them. Editors still manually choose and lay out news stories. Often, the front page changes only once a day, just like the print version, and it shows the same news to all readers.

There's no need for that uniformity. Every time a Web server generates a news page, for example, in response to a reader's clicking on a link, it can create that page from scratch. An online news site can change minute by minute. And it can even generate different front pages, essentially producing millions of distinct editions, each one targeting just one person -- you.

The most interesting and important way to customize a site is to create a page of stories based on your unique interests culled from information about your past reading behavior. There's already a model for that -- the recommendation systems used by Amazon, TiVo, and Netflix. Using information on past purchases, movie ratings, or items viewed, these systems steer consumers to items from among the thousands or millions they have on offer. Newspapers can and should borrow this idea.

It could transform the industry. Based on articles viewed, these systems could highlight the ones they think a reader would find most interesting, even presenting them in order, with the most interesting article first. No longer would readers have to skim pages of news to find what they needed. No longer would reporters have to battle for the limited space on the front page.
The article goes on to discuss recommender systems in general, the techniques used by Findory and Google News, and the long-term goal of personalizing information.


Anonymous said...

Great article in Spectrum on just how difficult it is to personalize a news site.

It's almost the opposite of the challenge that reporters and editors have in writing a story for their particular audience. For example, three NBC operations, CNBC, MSNBC, and NBC all cover an event from three different angles. This makes it especially important for a recommendation engine to analyze the body of all three stories to make the best recommendation.

I'm still hopeful that personalized news will be adopted more. It would be good for readers and for advertisers who understand the value of one-to-one marketing.

Anonymous said...

You're assuming that news organisations are interested in making the reading experience quick and efficient. Wrong. New organisations are there to serve their *advertisers* who gain most when the readers become distracted and click on more pages.

Anonymous said...


Good article. However, I didnt see any references to Loomia/Aggregate Knowledge. I have often clicked on Loomia reccomended links at WSJ (i.e. the box on the right which says- "People Who Read this Also read"

I think the Loomia approach is pretty cool and a good balance between complete personalization and reading what the "editor" thinks is important versus what the crowd thinks is important

Farrow said...

I am not sure what it looks like to the rest of the world, what with the license fee and all, but the BBC site, I think, has the best front page of any news organisation (well, it's more than that, but that is the focus of the website)