Chapter 4 – Recommending Movies Using Affinity Analysis

New datasets

http://www2.informatik.uni-freiburg.de/~cziegler/BX/

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/.

The Eclat algorithm

http://www.borgelt.net/eclat.html

The APriori algorithm implemented in this chapter is easily the most famous of the association rule mining graphs, but isn't necessarily the best. Eclat is a more modern algorithm...