There are many recommendation-based datasets that are worth investigating, each with its own issues.
URL: http://www2.informatik.uni-freiburg.de/~cziegler/BX/
Larger exercise!
There are many recommendation-based datasets that are worth investigating, each with its own issues. For example, the Book-Crossing dataset contains more than 278,000 users and over a million ratings. Some of these ratings are explicit (the user did give a rating), while others are more implicit. The weighting to these implicit ratings probably shouldn't be as high as for explicit ratings. The music website www.last.fm has released a great dataset for music recommendation: http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/.
There is also a joke recommendation dataset! See here: http://eigentaste.berkeley.edu/dataset/.
URL: http://www.borgelt.net/eclat.html
The APriori algorithm implemented here is easily the most famous of the association...