The matrix factorization method Macau in the Smurff 0.16.0 framework was used for Bayesian Matrix factorization. The model used four latent dimensions and 1200 training iterations using a probit noise model, of which 200 where burn-in samples and 1000 used for Bayesian sampling. We sampled every 5 th iteration, which yielded 200 predictions. The probability used for a label was the frequency that the label had been estimated to be the likely label in the 200 predictions.