  Model-based approaches to recommendation can recommend items with a very high
level of accuracy. Unfortunately, even when the model embeds content-based
information, if we move to a latent space we miss references to the actual
semantics of recommended items. Consequently, this makes non-trivial the
interpretation of a recommendation process. In this paper, we show how to
initialize latent factors in Factorization Machines by using semantic features
coming from a knowledge graph in order to train an interpretable model. With
our model, semantic features are injected into the learning process to retain
the original informativeness of the items available in the dataset. The
accuracy and effectiveness of the trained model have been tested using two
well-known recommender systems datasets. By relying on the information encoded
in the original knowledge graph, we have also evaluated the semantic accuracy
and robustness for the knowledge-aware interpretability of the final model.
