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1、How Computers Know What We Want - Before We DoHeres an experiment: try thinking of a song not as a song but as a collection of distinct musical attributes. Maybe the song has political lyrics. That would be an attribute. Maybe it has a police siren in it, or a prominent banjo part, or paired vocal h
2、armony, or punk roots. Any one of those would be an attribute. A song can have as many as 400 attributes - those are just a few of the ones filed under p.This curious idea originated with Tim Westergren, one of the founders of an Internet radio service based in Oakland, Calif., called Pandora. Every
3、 time a new song comes out, someone on Pandoras staff - a specially trained musician or musicologist - goes through a list of possible attributes and assigns the song a numerical rating for each one. Analyzing a song takes about 20 minutes.The people at Pandora - no relation to the alien planet - an
4、alyze 10,000 songs a month. Theyve been doing it for 10 years now, and so far theyve amassed a database containing detailed profiles of 740,000 different songs. Westergren calls this database the Music Genome Project.There is a point to all this, apart from settling bar bets about which song has the
5、 most prominent banjo part ever. The purpose of the Music Genome Project is to make predictions about what kind of music youre going to like next. Pandora uses the Music Genome Project to power whats known in the business as a recommendation engine: one of those pieces of software that gives you adv
6、ice about what you might enjoy listening to or watching or reading next, based on what you just listened to or watched or read. Tell Pandora you like Spoon and itll play you Modest Mouse. Tell it you like Cajun accordion virtuoso Alphonse “Bois Sec” Ardoin and itll try you out on some Iry LeJeune. E
7、nough people like telling Pandora what they like that the service adds 2.5 million new users a month.Over the past decade, recommendation engines have become quietly ubiquitous. At the appropriate moment - generally when youre about to consummate a retail purchase - they appear at your shoulder, whi
8、spering suggestively in your ear. Amazon was the pioneer of automated recommendations, but Netflix, Apple, YouTube and TiVo have them too. In the music space alone, Pandora has dozens of competitors. A good recommendation engine is worth a lot of money. According to a report by industry analyst Forr
9、ester, one-third of customers who notice recommendations on an e-commerce site wind up buying something based on them.The trouble with recommendation engines is that theyre really hard to build. They look simple on the outside - if you liked X, youll love Y! - but theyre actually doing something fie
10、ndishly complex. Theyre processing astounding quantities of data and doing so with seriously high-level math. Thats because theyre attempting to second-guess a mysterious, perverse and profoundly human form of behavior: the personal response to a work of art. Theyre trying to reverse-engineer the so
11、ul.Theyre also changing the way our culture works. We used to learn about new works of art from friends and critics and video-store clerks - from people, in other words. Now we learn about them from software. Theres a new class of tastemakers, and theyre not human.Learning to Love Dolph LundgrenPand
12、ora makes recommendations the same way people do, more or less: by knowing something about the music its recommending and something about your musical taste. But thats actually pretty unusual. Its a very labor-intensive approach. Most recommendation engines work backward instead, using information t
13、hat comes not from the art but from its audience.Its a technique called collaborative filtering, and it works on the principle that the behavior of a lot of people can be used to make educated guesses about the behavior of a single individual. Heres the idea: if, statistically speaking, most people
14、who liked the first Sex and the City movie also like Mamma Mia!, then if we know that a particular individual liked Sex and the City, we can make an educated guess that that individual will also like Mamma Mia!It sounds simple enough, but the closer you look, the weirder and more complicated it gets
15、. Take Netflixs recommendation engine, which it has dubbed Cinematch. The algorithmic guts of a recommendation engine are usually a fiercely guarded trade secret, but in 2006 Netflix decided it wasnt completely happy with Cinematch, and it took an unusual approach to solving the problem. The company
16、 made public a portion of its database of movie ratings - around 100 million of them - and offered a prize of $1 million to anybody who could improve its engine by 10%.The Netflix competition opened a window onto a world thats usually locked away deep in the bowels of corporate R&D departments. The eventual winner - which clinched the prize last fall - was a seven-man, four-country consortium called BellKors Pragmatic Chaos, which included Bob
