Hi Sebastian, I can only help you with what GenericItemBasedRecommender.mostSimilarItems() does. It's basically what you know from amazon.com: "People who like this item also like the following items". Mathematically spoken, you have a matrix of the preferences of users towards items and mostSimilarItems() searches the highest ranking item vectors using some similarity function (usually cosine or pearson correlation).
A good overview about how item-based collaborative filtering works and what the most similar items are can be found in this paper (helped me understand the whole issue): http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.9927&rep=rep1&type=pdf Regards, Sebastian Sebastian Feher schrieb: > Hi All, > > I'm looking at extracting association rules with Mahout. If I understand it > correctly, both GenericItemBasedRecommender.mostSimilarItems() and Parallel > FP-Growth seem to provide support for doing that. Is this true? If not what > are the major differences between the two (including scalability, > performance)? Thanks. > > Sebastian
