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 >> * >>
