Trust in music recommendation

Lately I've been thinking a great deal about the role of trust in music recommendation. It seems from some informal case studies (I talked to some non-MIR folk I work with at the radio station) that one key piece of the music recommendation puzzle is the trust embedded in the relationship between the the receiver of recommends and the entity doing the recommendation. If you think about the traditional means of doing recommendations, this makes sense. A radio DJ develops a (trust) relationship with her/his listeners. This lends a great deal of weight to the recommendations that are made in this context and the likelihood of their retention.

So this leads to a few questions:

1. Has this 'trust effect' in recommenders been studied at all? I'd be very interested to se some good pysch research on the topic.
   
2. Assuming it really does exist, how do we, as developers of automated tools integrate such a thing into recommendation engines? Is it really as simple as making the 'best' recommendations possible? (I think probably no...)
   
3. Putting together (1) and (2) what effect does 'developing a relationship over time' have on the viability of a recommendation engine?
   

Anyway, I believe this is one of the critical issues to the success of music recommenders in improving beyond 'sounds like' and 'others like you enjoy' type of things to a more dynamic and comprehensive recommendation service.

MyPySpace update

I have finally set down an intial release of the musicGrabber tool, which is being developed by myself and kurtjx over on sourceforge. You can now grab the python tool without getting your hand dirty in our subversion repository. Go take a look

A division of posting...

I think in the name of clarity I'll be using this space to post work and research related things, while leaving my original blog for personal things. I think this will be useful in the future.