Recommendation Engines
Recommendation Engines
are a type of software that let users rate items, so that other users may learn from the evaluation of th eothers.
Here's an excerpt from the New York Times, excerpted by Smart Mobs at http://www.smartmobs.com/archive/2006/01/23/ny_times_on_rec.html
See also Collaborative Filtering
"Mr. Hunt said NetFlix's recommendation system collected more than two million ratings forms from subscribers daily to add to its huge database of users' likes and dislikes. The system assigns different ratings to a movie depending on a particular subscriber's tastes. For example, "Pretty Woman" might get a four- or five-star rating if other people who share a customer's taste in movies rated it highly, while the same film might not appear on another customer's screen at all, presumably because other viewers with that customer's tastes did not rate it highly.
"The most reliable prediction for how much a customer will like a movie is what they thought of other movies," Mr. Hunt said. The company credits the system's ability to make automated yet accurate recommendations as a major factor in its growth from 600,000 subscribers in 2002 to nearly 4 million today.
Similarly, Apple's iTunes online music store features a system of recommending new music as a way of increasing customers' attachment to the site and, presumably, their purchases. Recommendation engines, which grew out of the technology used to serve up personalized ads on Web sites, now typically involve some level of "collaborative filtering" to tailor data automatically to individuals or groups of users.
Some engines use information provided directly by the shopper, while others rely more on assumptions, like offering a matching shirt to a shopper interested in purchasing a tie. And some sites are now taking personalization to another level by improving not only the collection of data but the presentation of it.
Liveplasma.com, an online site for music and, more recently, movies, graphically "maps" shoppers' potential interests. A search for music by Coldplay, for example, brings up a graphical representation of what previous customers of Coldplay music have purchased, presented in clusters of circles of various sizes.
The bigger the circle, the greater the popularity of that band. The circles are clustered into orbits representing groups of customers with similar preferences." (http://www.smartmobs.com/archive/2006/01/23/ny_times_on_rec.html)