But what was your input? Item item similarity? Then you already had item item similarity. And what you compute from that method is probably not meaningful.
You don't have a recommender problem so there is no question of what to feed to a recommender. Don't use it at all. You already have all you need in your ItemSimilarity. On Sep 27, 2012 7:50 PM, "Abhishek Roy" <[email protected]> wrote: > > Thanks Sean. I get your point. Will try incorporating that. > Earlier, as I mentioned, for a small item count(<5000), the > input(datamodel) to > the recommender was nC2 item-item pairs(tried to feed uniform preference > for > each item to every other item), without the rating field, and then called > recommender.mostSimilarItems() to get the list. nC2 works, but is not > scalable. > It worked well as the recommendations were the similar items(that works > for me > now). > Although am digging through the code to see what least input I can give, > any > meaningful suggestion for data input would be awesome. > > > >
