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 w

Answer 1

A fashionably late response: Pandora and Grooveshark are very different in the algorithm they use.

Basically there are two major approaches to recommendation systems - 1. collaborative filtering, and 2. content based. (and hybrid systems)

Most systems are based on collaborative filtering. This basically means matching lists of preferences): If I liked items A,B,C,D,E and F, and several other users liked A,B,C,D,E,F and J - the system will recommend J to me based on the fact that I share the same taste with these users (it's not that simple but that's the idea). The main features that are analyzed here are the items id and the users vote about these items.

Content based method analyze the content of the items at hand and build my profile based on the content of the items I like and not based on what other users like.

Having that said - Grooveshark is based on collaborative filtering Pandora is content based (maybe with some collaborative filtering layer on top).

The interesting thing about Pandora is that the content is analyzed by humans (musicians) and not automatically. They call it the music genome project (http://www.pandora.com/mgp.shtml), where annotators tag each song with a number of labels on a few axes such as structure, rhythm, tonality, recording technique and more (full list: http://en.wikipedia.org/wiki/List_of_Music_Genome_Project_attributes) That's what gives them the option to explain and justify the recommended song.