Hi Sean, I see your point. I think I better experiment with those different options.
I'd also like to ask if the result of LogLikelihoodSimilarity is between [0,1] ? It seems that I'm getting results higher than 1. So if like you said combining the different attributes can be done by multiplying them and normalizing them to [0,1] - what is the best method for normalization? * Agata Filiana Erasmus Mundus DMKM Student 2011-2013 <http://www.em-dmkm.eu/> * On 16 April 2013 12:30, Sean Owen <sro...@gmail.com> wrote: > Broadly the idea makes sense, but I think this is getting into hacking > heuristics together without a lot of principle. The result will > probably work, and you can just proceed as you say -- make up some > weights and use them to weight the various similarities. If you are > using the product of similarity values, you can compute something like > a weighted geometric mean. > https://en.wikipedia.org/wiki/Geometric_mean > > A step in a more principled direction is to consider these various > things as "items" -- things you read, hobbies you engage in, interests > you have. Then create a recommender on top of all of these things, > weighting the input differently. The often-mentioned ALS-WR is one of > several processes that fits since it has an explicit notion of input > weight. > > > On Tue, Apr 16, 2013 at 11:24 AM, Agata Filiana <a.filian...@gmail.com> > wrote: > > 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 > >> * > >> >
