Even though the idea of recommendations is anything but new, literally
nothing and nowhere works as expected. The only thing that comes close is based on the concept of neighbours, as implemented at Last.fm or RateYourMusic.
I don't understand why is it so hard to offer something along these lines:
1. Define dominant user preferences by clustering and segmenting the field of listened genres.
2. Build a list of relevant "neighbours":
2.1 Manually added users/friends
2.2 For each of the dominant genre preferences, find users with a high level of artist intersection within that genre and add them
3. Now, for a "find similar" query:
3.1 Define a reasonable time window
3.2 For each neighbour, find points in time when they listened to the queried track/artist
3.3 Build a list of tracks/artist from the defined window around the points found
3.4 Filter tracks/artists that are too "distant" on the general genre/tag map, or lie outside of the user's dominant preferences (with a degree of boundary feathering, perhaps)
3.5 Filter if similar to negative part of the query
3.6 If novelty is required: filter artists/tracks according to the degree of their presence in the user's history