On Thu, Feb 21, 2013 at 9:39 AM, Sean Owen <[email protected]> wrote: > It's also valid, yes. The difference is partly due to asymmetry, but also > just historical (i.e. no great reason).* The item-item system uses a > different strategy for picking candidates based on CandidateItemStrategy.* > > Where do I find more information about this? And thanks for the instantaneous reply :)
> > On Thu, Feb 21, 2013 at 2:37 PM, Koobas <[email protected]> wrote: > > > In the GenericUserBasedRecommender the concept of a neighborhood seems to > > be fundamental. > > I.e., it is a classic implementation of the kNN algorithm. > > > > But it is not the case with the GenericItemBasedRecommender. > > I understand that the two approaches are not meant to be completely > > symmetric, > > but still, wouldn't it make sense, from the performance perspective, to > > compute items' neighborhoods first, > > and then use them to compute recommendations? > > > > If kNN was run on items first, then every item-item similarity would be > > computed once. > > It looks like in the GenericItemBasedRecommender each item-item > similarity > > will be computed multiple times. > > (How much, depends on the data, but still.) > > > > I am wondering if anybody has any thoughts on the validity of doing > > item-item kNN in the context of: > > 1) performance, > > 2) quality of recommendations. > > >
