Sure, you don't need a recommender then. You are just computing most-similar items using some similarity metric. Forget the recommender and just loop over all items computing similarity to a given item and keep the top N. You can look at the mostSimilarItems() method for a template for how to do it.
I am not sure what item,item input you would construct to feed to a recommender -- this isn't a recommender problem. On Thu, Sep 27, 2012 at 7:14 PM, Abhishek Roy <[email protected]> wrote: > Sean Owen <srowen <at> gmail.com> writes: > > >> >> 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 <abhishekroy8 <at> gmail.com> > 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... >> > >> > >> > >> >> > > Sean, let me clarify. I am in a way, trying to recommend "similar" items to a > particular item. 100% Content based. > And I don't have the user,item,rating data. No user angle at all. No > "preference" angle at all. > > All I have is the set of all items in the system, and their > attributes(genre,title,description etc). I have read and realized that the > user,item,rating data can as well be : item,item data ...(rating/preference) > absent. Hence the confusion. So, in this case, what data do I give as an > input ? > Do I compute item,item entries based on a certain criteria ? What is the least > data I can give the system as an input to get my n most similar items based on > my custom ItemSimilarity ? > > >
