Joe on recommender systems:
We have, and this is work that dates back to '99 or so, studied explaining to users what the system was doing as a way of helping them understand whether they should trust the computer systems' recommendations and we found that most of the explanations that were intuitively appealing to a computer scientist, things that got into the statistics and the processing, completely turned off ordinary people. At the same time, really simple three point charts or analogies were much more compelling to the average user.
Joe on research recommenders:
I've got a student who's working with a couple of other people that built a prototype of a research paper recommender. You can tell it which papers you've already read and it will recommend papers that you should read next? He's actually now working with data from the ACM digital library to see what types of recommenders we can build that would help you discover that an article just published is something you should know about. You're doing research now in this new area? Here's a set of things to get you up to speed.
Pedro Domingos at the University of Washington has done some work showing how to analyze a population to find out which people you should give a free sample to if your goal is to spread positive word of mouth.