What is algorithm behind the recommendation sites like last.fm, grooveshark, pandora?

Question

I am thinking of starting a project which is based on recommandation system. I need to improve myself at this area which looks like a hot topic on the web side. Also wondering what is the algorithm lastfm, grooveshark, pandora using for their recommendation system. If you know any book, site or any resource for this kind of algorithms please inform.

Answer 1

Programming Collective Intelligence is a nice, approachable introduction to this field.

Answer 2

Pandoras algorithim started with just matching specific music genres to the certain song you inputed. Then it has been slowly growing by people voting if they like the song or dislike the song, enabling it to eliminate bad songs, and push good songs to the front. It also will sneek new songs that have few votes either up or down into your song playlist so that song can get some votes.

Not sure about the other sites listed.

Answer 3

Manning also has two good books on this subject. Algorithms of the Intelligent Web and Collective Intelligence in Action

Answer 4

There's a good demo video with explanation (and a link to the author's thesis) at Mapping and visualizing music collections. This approach deals with analyzing the characteristics of the music itself. Other methods, like NetFlix and Amazon, rely on recommendations from other users with similar tastes as well as basic category filtering.

Answer 5

Have a look at Collaborative filtering or Recommender systems.

One simple algorithm is Slope One.

Answer 6

Great paper by Yehuda Koren (on the team that won the Netflix prize): The BellKor Solution to the Netflix Grand Prize (google "GrandPrize2009_BPC_BellKor.pdf").

Couple websites:
Trustlet.org
Collaborative Filtering tutorials by Dr. Jun Wang

Google: item-based top-n recommendation algorithms