At a panel on social media for music at CitizenSpace last year, with discussion among musicians and passionate fans, musicians talked about their efforts to engage fans using emails about upcoming shows and recordings. But what did the fans want from musicians? Several people mentioned that what they appreciated most was music recommendations from musicians themselves.
This rang true to me. I’ve been finding wonderful music just by following musicians on Twitter, and also surfing the last.fm streams of people with distinctive sensibilities. What’s especially cool is that these recommendations are different from the standard marketing recommendations by genre – they aren’t tied to any genre in particular – punk americana musician listens to a series of classical requiems; a steampunk bigband leader listens to instrumentally interesting, intense pop.
These recommendations from people work much better for me than the algorithms in Pandora or Apples “genius”. Pandora finds music that has similar instruments, chords, volume, tempo, and other measurable characteristics. But people reveal music with whatever ineffable characteristics I was seeking. Pandora gets the sound and people get the soul.
It’s a bit of Silicon Valley heresy, perhaps, to be distrustful of algorithms that find things that are “interesting”. And I think that in some circumstances algorithms can find relevant information. Algorithms may be good in some circumstances, but human filters are great. Fundamentally, I suspect that the interestingness algorithm is Turing-complete – an algorithm that could really predict interestingness would have evolved intelligence and humanity.