Zhipeng's first answer is probably closest to a reason. You could do so, but it is of less use. You are already beginning from a constrained set of items, and neighborhood*s* around each of them may be too sparse to be useful. Not to mention that there are many of them.
But the neighborhood process is really just considering things with low similarity to have no weight (similarity). And something like that exists via CandidateItemStrategy. On Tue, Nov 27, 2012 at 2:59 AM, Evgeny Karataev <[email protected]> wrote: > Hello, > > Are there any particular reasons, why the implementation of item-based > recommendations does not support item neighborhood formation, e.g. based on > similarity threshold or by providing a number for the most similar items to > be used? > > I know that there is linear interpolation item-based recommendation, but it > seems a little bit different than just classical item-based recommendation. > And anyway it does not support threshold based neighborhood. > > Thank you. > > -- > Best Regards, > Evgeny Karataev
