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.
On Thu, Feb 21, 2013 at 2:37 PM, Koobas <koo...@gmail.com> 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. >
