Exploiting knowledge graphs for auto-encoding user ratings in recommender systems

In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, 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. 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 semantics-aware autoencoder, SEM-AUTO, able to extract and weight semantic features that can eventually be used to build a recommender system. We tested how our approach behaves in the presence of cold users on the MovieLens 1M dataset and compare results with BPRSLIM.