Would be fun to play around with a meta-recommender that compares recommendations from different services, for example, for a movie. So, I would be in one service, say Netflix and had hard time choosing a film between the big choice of movies. Instead of knowing what other Netflix users thought about the movie, I could also see how users in Yahoo!, MovieLens, etc rated it.
As we found out today, ratings, that are widely used in recommenders, are quite tricky things. There is a lot of sparsity problems, variations in ratings, interpretations of a rating scale, not enough criteria or too much of them, etc. So, it might be useful (or then totally not), to see how MovieLens and Amazon users rated the movie, to compare whether there is a deep variation between them, and eventually find a community whose tase is similar to yours.
Of course the datamodels are not the same and there are different rating scales, but some normalisation could be done or thumbs up/down, or so. Besides, comparing the evaluation data across different applications would probably yeld very interesting results!
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