User 1476 | 3/12/2015, 8:17:46 AM
Hi, I'm trying to create a recommender using Kiva's data dump with GraphLab Create 1.3.
The data contains pair-wise interactions of 'lender-loan' but with no rating, i.e. implicit. Each loan and lender has their own features.
I first tried item similarity recommender with only the pair-wise interaction data and got precision around 0.03-0.06. Then I switched to Ranking Factorization and tried different groups of features but ALWAYS got worse results around 0.0003. To recommend, the worse model will predict the same items for every user.
m = gl.recommender.rankingfactorizationrecommender.create(train, userid='lenderid', itemid='loanid', itemdata=loanfeature, userdata=lenderfeature, numfactors=20, regularization=0.1, binarytarget=True, max_iterations=20, solver = 'ials', verbose=True)
I think the result is not possible and always worse than traditional item-item collaborative filtering. And the absolute values of precision score are also very low. Is that normal? How can I diagnose the problem?
Thanks!!! Please tell me for any more code/info.