  An important problem in multiview representation learning is finding the
optimal combination of views with respect to the specific task at hand. To this
end, we introduce NAM: a Neural Attentive Multiview machine that learns
multiview item representations and similarity by employing a novel attention
mechanism. NAM harnesses multiple information sources and automatically
quantifies their relevancy with respect to a supervised task. Finally, a very
practical advantage of NAM is its robustness to the case of dataset with
missing views. We demonstrate the effectiveness of NAM for the task of movies
and app recommendations. Our evaluations indicate that NAM outperforms single
view models as well as alternative multiview methods on item recommendations
tasks, including cold-start scenarios.
