  Even in times of deep learning, low-rank approximations by factorizing a
matrix into user and item latent factors continue to be a method of choice for
collaborative filtering tasks due to their great performance. While deep
learning based approaches excel in hybrid recommender tasks where additional
features for items, users or even context are available, their flexibility
seems to rather impair the performance compared to low-rank approximations for
pure collaborative filtering tasks where no additional features are used.
Recent works propose hybrid models combining low-rank approximations and
traditional deep neural architectures with promising results but fail to
explain why neural networks alone are unsuitable for this task. In this work,
we revisit the model and intuition behind low-rank approximation to point out
its suitability for collaborative filtering tasks. In several experiments we
compare the performance and behavior of models based on a deep neural network
and low-rank approximation to examine the reasons for the low effectiveness of
traditional deep neural networks. We conclude that the universal approximation
capabilities of traditional deep neural networks severely impair the
determination of suitable latent vectors, leading to a worse performance
compared to low-rank approximations.
