Hi, Continuing this discussion - I have the implementation, but I'd like to know your opinion. As I said before, I am creating a new implementation of UserSimilarity as Sean pointed out. Does it make sense to put weights into these metrics? Say I combined 3 similarity metrics: reading history, hobbies and interests. I would like my recommender to be "based" on history but boosted with weighted hobbies and interests with different weight, for example interests is more important than hobbies.
Does that make sense? And how would you go about to implement it if it does make sense? Thank you again! * Agata Filiana Erasmus Mundus DMKM Student 2011-2013 <http://www.em-dmkm.eu/> * On 19 March 2013 12:03, Agata Filiana <a.filian...@gmail.com> wrote: > Ok, I will try that. > > Thanks for the help Sean! > > > On 19 March 2013 12:02, Sean Owen <sro...@gmail.com> wrote: > >> Write a new implementation of UserSimilarity that internally calls 2 other >> similarity metrics with the same arguments when asked for a similarity. >> Return their product. >> >> >> On Tue, Mar 19, 2013 at 6:59 AM, Agata Filiana <a.filian...@gmail.com >> >wrote: >> >> > I understand that, I guess what I am confused is the implementation of >> > merging the two similarity metrics in code. For example I apply >> > LogLikelihoodSimilarity for both item and hobby, and I have 2 >> > UserSimilarity metrics. Then from there I am unsure of how to combine >> the >> > two. >> > >> > >> > > > > -- > *Agata Filiana > * >
