This isn't a recommender problem -- it's simpler. It sounds like you just want to count the most frequently occurring items, and pairs of items. That's just a question of counting.
On Sun, Mar 11, 2012 at 12:32 PM, mahout user <mahoutu...@gmail.com> wrote: > Hello group, > > I am new to mahout..I am developing recommender with the help of > itemsimilarity.but i am confused for generating popular items among > them.which algorithm will calculate top popular items and frequently > bought together items from transactional record for the purpose of > displaying the recommended items to the users. > > if my sample dataset structure is given below. > > Dataset : > > UserId,#ItemId,#Preference > > thanks > kjon ynos
