  In the last decade, driven also by the availability of an unprecedented
computational power and storage capabilities in cloud environments we assisted
to the proliferation of new algorithms, methods, and approaches in two areas of
artificial intelligence: knowledge representation and machine learning. On the
one side, the generation of a high rate of structured data on the Web led to
the creation and publication of the so-called knowledge graphs. On the other
side, deep learning emerged as one of the most promising approaches in the
generation and training of models that can be applied to a wide variety of
application fields. More recently, autoencoders have proven their strength in
various scenarios, playing a fundamental role in unsupervised learning. In this
paper, we instigate how to exploit the semantic information encoded in a
knowledge graph to build connections between units in a Neural Network, thus
leading to a new method, SEM-AUTO, to extract and weigh semantic features that
can eventually be used to build a recommender system. As adding content-based
side information may mitigate the cold user problems, we tested how our
approach behave in the presence of a few rating from a user on the Movielens 1M
dataset and compare results with BPRSLIM.
