On Fri, Jan 16, 2015 at 9:58 AM, Zork Sail <zorks...@gmail.com> wrote: > And then train ALSL: > > val model = ALS.trainImplicit(ratings, rank, numIter) > > I get RMSE 0.9, which is a big error in case of preferences taking 0 or 1 > value:
This is likely the problem. RMSE is not an appropriate evaluation metric when you have trained a model on implicit data. The factorization is not minimizing the same squared error loss that RMSE evaluates. Use metrics like AUC instead, for example. Rating value can be 1 if you have no information at all about the interaction other than that it exists. It should be thought of as a weight. "10" means it's 10 times more important to predict an interaction than one with weight "1". --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
