  We review a method for click-through rate prediction based on the work of
Menon et al. [11], which combines collaborative filtering and matrix
factorization with a side-information model and fuses the outputs to proper
probabilities in [0,1]. In addition we provide details, both for the modeling
as well as the experimental part, that are not found elsewhere. We rigorously
test the performance on several test data sets from consecutive days in a
click-through rate prediction setup, in a manner which reflects a real-world
pipeline. Our results confirm that performance can be increased using latent
features, albeit the differences in the measures are small but significant.
