The input to the recommender remains the same -- user,item,rating. Your similarities are used as weights in a weighted average to make recommendations. This is unrelated -- or rather, not necessarily related at all -- to whatever custom similarity metric you create. Your similarities do not need to be precomputed. You could, but it's not necessary.
On Thu, Sep 27, 2012 at 6:48 PM, Abhishek Roy <[email protected]> wrote: > Sean Owen <srowen <at> gmail.com> writes: > >> >> File for FileDataModel? This does not change. But that input does not >> consist of item item pairs. Are you talking about something else? >> On Sep 27, 2012 5:10 PM, "Abhishek Roy" <abhishekroy8 <at> gmail.com> wrote: >> >> > Hi Sean, >> > For using a custom ItemSimilarity what should my data model file(item id1, >> > item >> > id2) include ? >> > >> > Please advise. >> > >> > Thanks, >> > Roy >> > >> > >> > > Thanks for the quick response Sean. > My end goal (short term) is to show "related / similar" items for my site when > the user(any user, including unregistered user) is looking at a particular > item. > Basically I am looking at (rather created) a custom ItemSimilarity using > domain > specific attributes that computes a similarity score between a pair of items. > I > am using a GenericItemBasedRecommender and then calling n mostSimilarItems() > to > get my recommendations. The problem is, and I didn't see anything on that in > the > book as well as the forum, that I am not sure about the data model to feed to > the GenericItemBasedRecommender. I did a brute force, computed > nC2(combinations) > of {item id, item id} pairs and fed that as the data model. Works, but > definitely not scalable and sensible. What data model does this kind of a > system > need ? I am not having preference data(very little), and since this is content > based recommendation, am puzzled about the data to be encapsulated by the > datamodel. I hope I am clear.. > Please suggest... > > >
