This sounds like a bug ... Did you try a different lambda? It would be great if you can share your dataset or re-produce this issue on the public dataset. Thanks! -Xiangrui
On Thu, Mar 26, 2015 at 7:56 AM, Ravi Mody <rmody...@gmail.com> wrote: > After upgrading to 1.3.0, ALS.trainImplicit() has been returning vastly > smaller factors (and hence scores). For example, the first few product's > factor values in 1.2.0 are (0.04821, -0.00674, -0.0325). In 1.3.0, the > first few factor values are (2.535456E-8, 1.690301E-8, 6.99245E-8). This > difference of several orders of magnitude is consistent throughout both user > and product. The recommendations from 1.2.0 are subjectively much better > than in 1.3.0. 1.3.0 trains significantly faster than 1.2.0, and uses less > memory. > > My first thought is that there is too much regularization in the 1.3.0 > results, but I'm using the same lambda parameter value. This is a snippet of > my scala code: > ..... > val rank = 75 > val numIterations = 15 > val alpha = 10 > val lambda = 0.01 > val model = ALS.trainImplicit(train_data, rank, numIterations, > lambda=lambda, alpha=alpha) > ..... > > The code and input data are identical across both versions. Did anything > change between the two versions I'm not aware of? I'd appreciate any help! > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
