If all of your similarities are a product like this, then they're all "low". In a relative sense this is fine. But this is also why I proposed a geometric mean instead. For example the geometric mean of these is about 0.424 and this notion can be extended to include weights as well, which is what may make it particularly interesting to you since you mentioned weighting.
On Wed, Apr 17, 2013 at 3:56 PM, Agata Filiana <[email protected]> wrote: > Just a thought, when you say to combine the metrics by multiplying their, > for example Sim1 = 0.9 and Sim2 = 0.2 > When they are multiplied it would give a result of 0.18 which is very low, > remembering that they are pretty "similar" based on Sim1 - how can this > problem be tackled? > > * > > Agata Filiana > Erasmus Mundus DMKM Student 2011-2013 <http://www.em-dmkm.eu/> > * > > > On 16 April 2013 16:41, Agata Filiana <[email protected]> wrote: > >> Thanks a lot for the insight,very useful! >> >> >> * >> >> Agata Filiana >> Erasmus Mundus DMKM Student 2011-2013 <http://www.em-dmkm.eu/> >> * >> >> >> On 16 April 2013 16:40, Sean Owen <[email protected]> wrote: >> >>> Of course it's not meaningless. They provide a basis for ranking >>> items, so you can return top-K recommendations. >>> If it's normally based on similarity and ratings -- and you have no >>> ratings -- similarity is of course the only thing you can base the >>> result on. >>> >>> On Tue, Apr 16, 2013 at 3:36 PM, Agata Filiana <[email protected]> >>> wrote: >>> > Well right now, I am only using one boolean file -just from from this >>> > history of reading. >>> > So you are saying the values generated in >>> > the GenericBooleanPrefUserBasedRecommender is actually useless in this >>> case >>> > of no ratings and that it is merely based on the similarity only? >>> >> >>
