You could make an ItemSimilarity based on the tags. Maybe you use the Tanimoto coefficient to make a simple similarity metric based on their presence or absence. Then you use an item-based recommender.
On Thu, May 10, 2012 at 5:07 PM, Jahangir Mohammed <[email protected]> wrote: > Thanks Sean for response. Let say, I have tags associated to the items > where items are more like an e-commerce(electronics, less than 20$, best > buy product,..so on) and I have some users demographic information. So, I > was thinking that I can write UserSimilarity and ItemSimilarity based on > these. > > On Thu, May 10, 2012 at 11:52 AM, Sean Owen <[email protected]> wrote: > >> The best, or perhaps only, way to integrate such information is to >> implement your own version of UserSimilarity or ItemSimilarity and >> then use a user-based or item-based recommender. You can implement >> whatever similarity rule you think is best according to your metadata. >> There's not a lot to be said unless you can say more about exactly >> what metadata you have. >> >> On Thu, May 10, 2012 at 4:31 PM, Jahangir Mohammed >> <[email protected]> wrote: >> > I followed very much the example as given in the documentation and it's >> > working as expected so far. >> > >> > Have a question on how I can use some of the metadata I have for users >> and >> > items to still improve the recommendations. It looks obvious how to do >> it, >> > but still want to get a heads up from people who have been using taste >> for >> > a while. So, is my assumption correct that I have to implement >> > UserSimilarity and ItemSimilarity interfaces? >> > >> > Thanks in advance for any suggestions. >> > >> > Thanks, >> > Jahangir. >>
