IT MAY SEEM A SMALL STEP from recommending products to recommending information. In fact, doing so is actually quite complex. Stand at the entrance of a Wal-Mart or look at Amazon’s home page and the shiny world of each one’s wares seems limitless. But it’s not. It is firmly bounded by the constraints of time and warehouse space. A sprawling Wal-Mart store typically has about 100 000 items; Amazon carries a few million. The world of information, on the other hand, is measured in billions of pages and peta­bytes of data. Processing data on this scale can require a supercomputer-scale infrastructure well beyond the means of a city newspaper.

Recommender systems also face what is known as the cold-start problem, which stems from the difficulty of rating any item that either has not yet attracted the notice of ­recommenders or has attracted only those about whom nothing is known. For example, before a new movie is previewed by ­critics, no one at all has seen it, so no one can recommend it. Within weeks, though, enough people will have contributed opinions to help many others decide whether to see it. But a news article doesn’t have weeks to attract attention, only hours. Often, by the time a fair number of people have read the article, it may well have faded into irrelevance. As we’ll see, one of Findory’s goals was to ameliorate the cold-start problem.

To understand how a really successful recommendation system for news might work, first consider those being used now at sites like Amazon and Netflix. One of the fundamental characteristics of these systems is that they learn not just from your behavior but also from that of other customers. The underlying assumption is that there are other people out there who are like you and that those people have found and enjoyed things you haven’t yet seen. These algorithms search over Web-site logs, ratings, and purchase transactions to discover people with interests similar to your own. Then the algorithms look up the items those people liked and recommend them to you.

Suppose that many people who buy the textbook Managing Gigabytes also buy Lucene in Action; the algorithm will conclude that the books may be similar, particularly if the people who buy Managing Gigabytes buy Lucene in Action much more frequently than the general population does. Even if the books are on different topics and the texts of the books are not similar, purchases in common reveal books with similar appeal. People who buy books on information technology may, for instance, also tend to buy science fiction.

More generally, if the recommendation system can find users who have bought many things you have bought, then it will bring to your attention things these other people have bought that you have not. This kind of algorithm is often referred to as collaborative or social filtering because it uses the preferences of like-minded people in the community to filter and prioritize what you see.

Because it’s so difficult to apply recommendation algorithms to information, sites have tried to personalize news in other ways. One of the largest customizable news sites, My Yahoo, launched in July of 1996. A user chooses from hundreds of modules—including news, weather, sports scores, and stock prices—and picks the layout of these modules on the page. User-customized sites are simple to build, easy for readers to understand, and take advantage of the ability of online news sites to show a different front page to each reader. By customizing, ­readers can emphasize the news most important to them.

Unfortunately, most readers don’t. Research at Yahoo has found that most users do not customize their front pages and that most who do don’t bother to update those pages to reflect their changing interests.