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!
