Xikipedia

Xikipedia (pronounced as either "cheek-ee-peedia or chick-ee-peedia") is a multilingual, web-based, free-content "pedia thingy", written collaboratively by errr... volunteers? Welcome to the world of "Xi!" (pronounced "chee").

Saturday, January 07, 2006

Excelente! Quando chegarem adicionem-me como amiga!!!

3 Comments:

At 9.1.06, boneca said...: conta-me mais!..
At 9.1.06, Spirale said...: O link: http://www.last.fm/
At 9.1.06, Spirale said...: O artigo in Wired News:

Last.fm: Music to Listeners' Ears
By Leander Kahney Leander Kahney

In this age of locked-down radio playlists, in which the same songs are played over and over, discovering new music is harder than ever.

But a new online radio station out of London is attempting to tackle the problem by automatically tailoring the music it plays to individual listeners' tastes.

Last.fm is a streaming radio station with a built-in collaborative filter that attempts to learn its listeners' likes and dislikes. Based on data gathered, the station delivers a personalized radio stream to each of its listeners.

Collaborative filtering is a widely used profiling technique found in systems such as Amazon.com's "personalized recommendation" system and the auto-record feature in TiVo's digital video recorders.

Basically, collaborative filters compare people's tastes. Such a system notes what you buy or record, and compares that data to the records of other people in the system. Then it's able to suggest items you might like that you previously have not heard or seen.

If there's a high degree of overlap between what you and others like, it's a good bet you will like something they recommend because you have similar tastes.

Here's how it works for Last.fm: Users can either fill out a profile or just begin listening. If a song plays to the end, the system logs this as a thumbs up. But if the user doesn't like a song and hits the Change button in the Last.fm player, it's marked as a thumbs down.

Over time, a preference profile is built. By comparing the preference profiles of its listeners, the system can predict what songs a particular listener might like, based on the overlap with other listeners with similar tastes.

"It is all very intuitive," said one of Last.fm's co-founders, Michael Breidenbruecker. "If you don't like what you hear, press the Change button. It's like flipping radio channels, or zapping TV. The emphasis is on enjoying the music. The recommendation aspect is hidden behind the actual consumption of music."

Technology pundit Clay Shirky has predicted that services like Last.fm will be "revolutionary."

In a January 2003 paper called "The Music Business and the Big Flip," Shirky wrote that instead of record labels filtering what people hear, the industry should be throwing everything into the public domain and allowing the public to choose what is popular through systems like collaborative filtering.

"The industry harvests the aggregate taste of music lovers and sells it back to us as popularity, without offering anyone the chance to be heard without their approval," Shirky wrote. "The industry's judgment, not ours, still determines the entire domain in which any collaborative filtering will subsequently operate. A working 'publish, then filter' system that used our collective judgment to sort new music before it gets played on the radio or sold at the record store would be a revolution."

Shirky could not be reached for comment for this article.

Last.fm is not the first project to apply collaborative filtering to music. In the late 1990s, about half a dozen companies, including the high-profile Firefly, tried to build music-recommendation systems based on lists of users' preferred songs or bands.

Since then, a lot of companies in the digital music business –- from jukebox publishers to streaming radio stations -- have experimented in one way or another with collaboratively filtered music.

Most have offered lists of recommendations to users based on their stated preferences. None of the services, however, proved very popular, largely because they required so much work from their users.

Like Last.fm, some streaming radio stations -- such as Yahoo's Launchcast streaming radio service -- have used collaborative filtering to match streams to individual listeners' tastes.

However, unlike Last.fm, but in line with past efforts, Launchcast users must manually rate songs to build their preference profiles. With Last.fm, listeners' preferences are automatically inferred from their listening behavior.

Since its public launch around Christmas, Last.fm has grown to about 6,000 registered users. The service offers about 30,000 tracks from all kinds of musical genres, ranging from classical to avant-garde electronica.

Breidenbruecker said the station is beginning to be sought out by smaller, independent labels because they see its profiling system as a way to reach audiences that otherwise wouldn't hear their music.

"There is no better way to target music," he said. "That's what Last.fm is about -- delivering the right music to the right ears."

The system appears to be catching on with some listeners.

A user called Christelle wrote on the site, "Definitely the most interesting concept for radio recently."

Another, named Yaxu, said, "Last.fm, you are the soothing ointment that I apply to the horrid rash of Internet radio."