The newspaper, that daily chronicle of human events, is undergoing the most momentous transformation in its centuries-old history. The familiar pulp-paper product still shows up on newsstands and porches every morning, but online versions are proliferating, attracting young readers and generally carving out a sizable swath of the news business. In the United States alone, 34 million people have made a daily habit of reading an online newspaper, according to the Newspaper Association of America.
It’s just the beginning. Online news will inevitably grow at the expense of its traditional counterpart because the Web not only lowers production and distribution costs, it also opens up newspapers to entirely new formats. Even run-of-the mill Web servers with access to a reasonable supply of news stories can generate thousands of different versions of a newspaper. Yet 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. Unless and until they do so, online newspapers will become increasingly irrelevant as the stories that are important to you get buried in an Internet already filled with absurdly more information than any one person can use.
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.
In their uphill battle to stay relevant, newspapers will first have to catch up with other news sites that already customize their front pages in one way or another. Aggregators such as Google News, My Yahoo, and Netvibes allow a reader to configure the layout of his or her personal page so that it highlights the most popular or highly regarded news. These sites also cluster news by topic or category and let readers focus on the articles that interest them the most. Such innovations are useful, but they still fall short of what’s needed. My Yahoo, for example, requires users to configure the page themselves and to make changes when their interests do, instead of accurately inferring those changes from whatever has attracted the user’s attention lately.
Google News is the best of the bunch, a popular news site that does use software to automate the prioritizing and laying out of stories. It changes rapidly, clusters stories that focus on the same event, allows users to customize the site, and recommends news based on past reading habits. But news sites could do even better by automatically learning what news stories each reader wants and using that knowledge to “print” millions of personalized editions of the newspaper.
Such features aren’t far off—they were actually part of a news aggregation Web site called Findory.com, which I ran between 2004 and 2007. Findory built a unique, personalized front page for each reader, based on what he or she had read in the past. In so doing it showed a way by which newspapers could recommend information much as Amazon recommends books.
Newspapers constitute a US $55 billion business in the United States, yet that business is invariably described as troubled. Many readers still feel loyalty to their hometown newspaper and know it is likely to contain news relevant to them, but they are increasingly reluctant to wade through all its articles to find the few that matter to them. Personalized news recommendations can be a lifesaver to newspapers that are drowning in the sea of information that washes over us all.