Robin, Sebastian, Sean, thanks for your responses. Yes that is exactly what I am looking for: computing frequent item sets based on co-browse, co-purchase, co-searching, user-item ratings and other user-item activities and then use these frequent item sets to provide recommendations for an active item and/or an active user.
Regarding the GenericItemBasedRecommender.mostSimilarItems() I've used both Tanimoto and also defined a custom similarity function that works the same way to my current custom coded frequent item sets algorithm that I'm trying to replace and test with Mahout. There are a few questions that I'm not able to answer: - do you support cross-type frequent item sets? for example - people who Browsed this item - ended up purchasing these items. In this case the item pairs are generated by taking one item from the Browse space and the other from Purchase space. Is this something that can be achieved with the current algorithms(GenericItemBasedRecommender.mostSimilarItems(), FP-Growth) in there existing form and if not there an extension mechanism that allows me to do that in a clean fashion or do I have to modify the algorithm code? Thanks On Apr 14, 2010, at 11:46 AM, Sebastian Schelter wrote: > 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 >
