To be clear, AudioScrobbler is the tech built by Last.fm to run their service. They collect stats on the tracks which people listen to (also 'Like's of tracks and artists).
So Last.fm does social similarity... users who listened to X also listened to Y - you like X so maybe you will also like Y.
Given a large enough user base submitting stats, social similarity is likely to provide better results than computer analysis approaches. For example, try querying the Last.fm API for similar artists to someone you know - probably comes up with some good matches and a few obscure or oddball ones, which nonetheless reflect real people's listening habits. The more obscure the artist you search for the more likely you'll get weird matches.
Even if you could get the automatic genre classification method described by George Tzanetakis to work well you are missing out on the subjective judgements of quality supplied by real people. eg two tracks both look like 'Jazz' but there are many different kinds of Jazz... and I might be interested in non-Jazz albums that a favourite jazz musician has played on. Social similarity would be more likely to capture that info.
