- So this leads to a few questions:
- Has this 'trust effect' in recommenders been studied at all? I'd be very interested to se some good pysch research on the topic.
- 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...)
- 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.